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Do GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents

Summary

This paper introduces CONFLICTGUI, a benchmark for studying conflict-aware termination in GUI agents. It finds that agents exhibit severe execution-biased overcompliance, blindly executing infeasible instructions. The authors propose CONFLICTGUARD, an inference-time framework that aligns feasibility awareness with action generation and significantly improves conflict-task success across five agents while preserving normal-task performance.

SourcearXiv AIAuthor: Zhaoyuan Huang, Tianjie Ju, Pengzhou Cheng, Zheng Wu, Yansi Li, Chuanbiao Song, Jun Lan, Huijia Zhu, Weiqiang Wang, Zhuosheng Zhang
Do GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents
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[Submitted on 3 Sep 2026]

Title:Do GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents

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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 overcompliance: 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 agents from over-compliant execution 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.

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2609.03438 [cs.AI]

(or arXiv:2609.03438v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.03438

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zhaoyuan Huang [view email] [v1] Thu, 3 Sep 2026 06:48:41 UTC (1,780 KB)

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Key points and analysis

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Key points

  • CONFLICTGUI evaluates GUI agents on instruction-internal conflicts and instruction-GUI context conflicts.
  • Existing agents show execution-biased overcompliance, continuing to act under conflicting instructions.
  • CONFLICTGUARD combines feasibility verification with conditional action modulation at inference time.
  • Testing on five common agents shows large gains on conflict tasks with minimal impact on normal tasks.

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