GUI agent: Guided Exploration of User-Sensitive Screens
On the map: LLM agents and tool use → GUI and mobile agents
Abstract (arXiv)
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.
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 22 of its 38 references that are in the map. First posted on arXiv 24 Jun 2026.
Builds on
22 of 38 references.
Closest ancestors · 7
Same direction, newest first
- 2025Mobile-Agent-v3: Fundamental Agents for GUI Automation21 Aug
- 2025GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent22 May
- 2025GUI-Xplore: Empowering Generalizable GUI Agents with One Exploration22 Mar
- 2025UI-TARS: Pioneering Automated GUI Interaction with Native Agents21 Jan
- 2024SPA-Bench: A Comprehensive Benchmark for SmartPhone Agent Evaluation19 Oct
- 2024Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration3 Jun
- 2024Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perception29 Jan
Other references · 15
- 2023ExpeL: LLM Agents Are Experiential Learnerscited by 876
- 2023Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Modelscited by 574
- 2024GPT-4V(ision) is a Generalist Web Agent, if Groundedcited by 563
- 2024AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agentscited by 468
- 2024Agent Q: Advanced Reasoning and Learning for Autonomous AI Agentscited by 208
- 2024Tree Search for Language Model Agentscited by 151
- 2024The Emerged Security and Privacy of LLM Agent: A Survey with Case Studiescited by 131
- 2024Dissecting Adversarial Robustness of Multimodal LM Agentscited by 120
- 2024NNetNav: Unsupervised Learning of Browser Agents Through Environment Interaction in the Wildcited by 59
- 2023Explore, Select, Derive, and Recall: Augmenting LLM with Human-like Memory for Mobile Task Automationcited by 58
- 2024Enhancing Decision-Making for LLM Agents via Step-Level Q-Value Modelscited by 28
- 2025Monte Carlo Planning with Large Language Model for Text-Based Game Agentscited by 22
3 more
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2025A Survey on (M)LLM-Based GUI Agents
- 2025Toward a Human-Centered Evaluation Framework for Trustworthy LLM-Powered GUI Agents
- 2025The Obvious Invisible Threat: LLM-Powered GUI Agents' Vulnerability to Fine-Print Injections
- 2025VeriOS: Query-Driven Proactive Human-Agent-GUI Interaction for Trustworthy OS Agents
- 2024GUI Agents with Foundation Models: A Comprehensive Survey
- 2025GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies
- 2024Large Language Model-Brained GUI Agents: A Survey
- 2025ScreenLLM: Stateful Screen Schema for Efficient Action Understanding and Prediction