GUI agent: Guided Exploration of User-Sensitive Screens

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Aradhana Nayak, Mussadiq Nazeer, Wang Peng, Feng Liu
• Venue: arXiv: cs.AI
• Published: 2026-06-24
• Collected: 2026-06-25
• URL: http://arxiv.org/abs/2606.25705v1
• PDF: https://arxiv.org/pdf/2606.25705v1
• DOI: UNKNOWN

Plain-Language Brief

This research presents a new agent called Guided Exploration of User-Sensitive Screens (GUI agent) designed to automate tasks within an open Graphical User Interface (GUI) environment. The GUI agent is intended to identify user-sensitive states and queries, requesting handover to the user in critical scenarios. This is aimed at improving the reliability of large language model (LLM)-driven agents in real-world deployment.

What the Document Says

• The research focuses on an explorer agent for LLM-driven agents in a GUI environment.
• The goal is to identify user-sensitive states and queries.
• The identified queries lead to user-sensitive states in the GUI environment.
• The agent requests handover to the user in critical scenarios.

Key Concepts Mentioned

• LLM agents
• GUI environment
• user-sensitive information
• handover

Claims Made by the Document

• LLM-driven agents encounter user-sensitive information and require takeover by the user.
• State-of-the-art LLM-driven agents are fine-tuned to complete tasks without considering safety implications.
• Real-world deployment of these agents is difficult and affects reliability.
• It's crucial to identify and categorize user-sensitive states and queries for engineers.

Evidence Used

• The text directly mentions the goals and objectives of the research.

Methods / Approach

• Systematic exploration of the query space starting from one demonstrated task.

Limitations Stated by the Authors

The paper does not discuss the specific methods used to identify user-sensitive queries or states.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The agent's performance and effectiveness in real-world scenarios is unknown.

Open Questions

• How does the GUI agent identify user-sensitive queries or states?
• What are the specific methods used for this identification process?
• How effective is the GUI agent in improving the reliability of LLM-driven agents?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The paper focuses on developing an explorer agent but does not discuss its performance or effectiveness.

Raw Abstract / Excerpt

LLM agents are increasingly being used to automate tasks for users within an open GUI environment. They inevitably encounter screens containing user-sensitive information, for which takeover of task execution by the user is highly desirable or even necessary. State-of-the-art LLM-driven agents are usually fine-tuned to complete tasks regardless of the safety implications of their actions. This makes their real-world deployment difficult and adversely affects the reliability. Therefore, it is crucial to identify and categorize user-sensitive states and define user-sensitive queries. This dataset would be to engineers to recognize and request handover to the user in critical scenarios. This short paper develops an explorer agent that systematically explores the query space starting from one demonstrated task to identify queries that, if executed, would lead to user-sensitive states in a GUI environment.