UFO: A UI-Focused Agent for Windows OS Interaction
Not on the map. Why?
Cited by 95 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision. UFO employs a dual-agent framework to meticulously observe and analyze the graphical user interface (GUI) and control information of Windows applications. This enables the agent to seamlessly navigate and operate within individual applications and across them to fulfill user requests, even when spanning multiple applications. The framework incorporates a control interaction module, facilitating action grounding without human intervention and enabling fully automated execution. Consequently, UFO transforms arduous and time-consuming processes into simple tasks achievable solely through natural language commands. We conducted testing of UFO across 9 popular Windows applications, encompassing a variety of scenarios reflective of users' daily usage. The results, derived from both quantitative metrics and real-case studies, underscore the superior effectiveness of UFO in fulfilling user requests. To the best of our knowledge, UFO stands as the first UI agent specifically tailored for task completion within the Windows OS environment. The open-source code for UFO is available on https://github.com/microsoft/UFO.
Key facts. Cited by 147 papers in the map (data through 7 Oct 2026). Builds on 32 of its 47 references that are in the map. First posted on arXiv 8 Feb 2024.
Builds on
32 of 47 references.
References · 32
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2023GPT-4 Technical Reportcited by 18,132
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- 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
- 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,179
- 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
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- 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
- 2023Reasoning with Language Model is Planning with World Modelcited by 846
- 2023ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debatecited by 724
- 2023CogAgent: A Visual Language Model for GUI Agentscited by 692
- 2023The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)cited by 677
- 2024GPT-4V(ision) is a Generalist Web Agent, if Groundedcited by 547
- 2023AppAgent: Multimodal Agents as Smartphone Userscited by 407
- 2023Character-LLM: A Trainable Agent for Role-Playingcited by 335
- 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agentscited by 329
- 2024Mobile-Agent: Autonomous Multi-Modal Mobile Device Agent with Visual Perceptioncited by 283
- 2023AutoAgents: A Framework for Automatic Agent Generationcited by 211
- 2024Agent AI: Surveying the Horizons of Multimodal Interactioncited by 167
- 2023GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigationcited by 137
- 2023GPT-4V(ision) as a Generalist Evaluator for Vision-Language Taskscited by 118
- 2023TaskWeaver: A Code-First Agent Frameworkcited by 85
- 2023Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosiscited by 77
- 2023GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstrationcited by 76
- 2023Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generationcited by 73
- 2023Xpert: Empowering Incident Management with Query Recommendations via Large Language Modelscited by 22
- 2023Assess and Summarize: Improve Outage Understanding with Large Language Modelscited by 17
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Citing papers per year: 2024: 46; 2025: 70; 2026: 31.
147 papers.
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