Challenge: UFO is a UI-Fcused agent designed to fulfill user requests tailored to Windows OS applications . it decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications.
Approach: They propose a UI-Fcused Windows OS agent that decomposes user requests using a divide-and-conquer approach and incorporates a control interaction module tailored for Windows OS.
Outcome: The proposed agent decomposes user requests using divide-and-conquer approach, enabling seamless navigation and addressing sub-tasks across multiple applications.

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Challenge: Existing GUI agents perform poorly on multi-application tasks, stalling at early sub-goals.
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GUI Agents: A Survey (2025.findings-acl)

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Challenge: Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life.
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OS Agents: A Survey on MLLM-based Agents for Computer, Phone and Browser Use (2025.acl-long)

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Challenge: a new generation of (M)LLMs is enabling the creation of superintelligent AI assistants . OS Agents can complete tasks autonomously and have the potential to significantly enhance the lives of billions of users worldwide.
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AXIS: Efficient Human-Agent-Computer Interaction with API-First LLM-Based Agents (2025.acl-long)

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Challenge: Multimodal large language models (MLLMs) have enabled LLM-based agents to directly interact with application user interfaces (UIs), enhancing agents’ performance in complex tasks.
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Challenge: Existing approaches to creating autonomous graphical user interfaces rely on external tools and application-specific APIs to interpret the environment.
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Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.acl-long)

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Challenge: Current language model-driven agents lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions.
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SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents (2024.acl-long)

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Challenge: Existing GUI agents interact with the environment through extracted structured data, which can be notably lengthy (e.g., HTML) and occasionally inaccessible (e-book).
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OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)

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Challenge: Existing GUI agents for real desktop environments require large amounts of high-quality interaction data, but collecting human demonstrations is expensive.
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OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents (2025.findings-acl)

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Challenge: Existing efforts to build GUI agents focused on the autonomous mode have failed to address the problem of over-execution.
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