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

arXiv: https://arxiv.org/abs/2609.03438 · HTML: https://mlsanity.com/paper/2609.03438/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in GUI and mobile agents, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 44 references that are in the map.
- First posted on arXiv 3 Sep 2026.

## Placed in

- LLM agents and tool use / [GUI and mobile agents](https://mlsanity.com/agents/gui-and-mobile-agents/)

## Builds on (most cited first, 20 of 20)

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- [VeriOS: Query-Driven Proactive Human-Agent-GUI Interaction for Trustworthy OS Agents](https://mlsanity.com/paper/2509.07553/) (2025)
- [VenusBench-GD: A Comprehensive Multi-Platform GUI Benchmark for Diverse Grounding Tasks](https://mlsanity.com/paper/2512.16501/) (2025)
- [Activation Steering Meets Preference Optimization: Defense Against Jailbreaks in Vision Language Models](https://mlsanity.com/paper/2509.00373/) (2025)
- [See, Think, Act: Teaching Multimodal Agents to Effectively Interact with GUI by Identifying Toggles](https://mlsanity.com/paper/2509.13615/) (2025)
- [Faithful Mobile GUI Agents with Guided Advantage Estimator](https://mlsanity.com/paper/2605.01208/) (2026)

## Abstract (arXiv)

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.