Paper Overview
Field: AI/ML Authors: Zhaoyuan Huang, Tianjie Ju, Pengzhou Cheng Release date: 2026-09-06 arXiv: 2509.00009
Summary
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, the authors introduce CONFLICTGUI, a benchmark covering instruction-internal conflicts and instruction-GUI context conflicts to study conflict-aware termination.
Key Findings
- Evaluation reveals severe execution-biased overcompliance: agents that perform well on feasible tasks often continue to execute blindly under conflicting instructions.
- Feasibility verification protocol: guides the agent to evaluate instruction logic and GUI-side evidence before acting.
- Conditional action modulation mechanism: shifts the agent from overcompliant execution toward termination-oriented behavior.
Proposed Solution: CONFLICTGUARD
To mitigate this behavior, the authors propose CONFLICTGUARD, an inference-time framework that aligns an agent's feasibility awareness with its action generation. It consists of two coupled components:
Results
Experiments across five widely used agents show that CONFLICTGUARD significantly improves average conflict-task success rates while maintaining normal GUI task performance. These results validate that lightweight inference-time interventions can substantially enhance GUI agents' ability to recognize inappropriate execution scenarios and avoid unnecessary actions.
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*Auto-collected on 2026-09-06*