Paper Overview
- Field: AI/ML
- Authors: Zhaoyuan Huang, Tianjie Ju, Pengzhou Cheng
- Published: 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.
The authors introduce CONFLICTGUI, a benchmark covering instruction-internal conflicts and instruction-GUI context conflicts to study conflict-aware termination. Their evaluation reveals severe execution-biased overcompliance: agents that perform well on feasible tasks often continue to execute blindly under conflicting instructions.
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 contains two coupled components:
1. Feasibility verification protocol — guides the agent to evaluate instruction logic and GUI-side evidence before acting. 2. Conditional action modulation — shifts the agent from overcompliant execution toward termination-oriented behavior.
Results
Experiments on 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 intervention can substantially enhance GUI agents' ability to recognize inappropriate execution scenarios and avoid unnecessary actions.
--- *Auto-collected on 2026-09-06*