Meet the PC-AGENT: Hierarchical multi-agent team category Cutical Task Agentent Agentent Agentent Automation in PC

Hundreds of large languages ​​(MLLMs) show amazing skills in various stores, which processes their appearance to the Multi-Modal Agents for human relief. GUI Aalities of PCs are facing difficult challenges especially when compared with smartphone partners. PC areas are very complex practical materials with crowded icons, various thumbnails and widgets that are usually lacking text, leading to intelligent crisis. Even advanced models like Clause-3.5 reach only 24.0% accuracy in the activities of grave gui. Also, PC production activities include a wirefacing flow consisting of many programs in a remote functionality and reliance, which causes the amazing decrease when 41.8% at 81% at 81% in total instructions.

Previous ways have developed structures for dealing with the difficulty of PC work with various strategies. The UFO uses the formation of agents of agents agents to separate the selection from direct control. In the meantime, agents increases planning skills by integrating online search for local memory. However, these methods display important restrictions on restrictions and the operation of the text on the screen – the critical need for production conditions such as the document. In addition, they often fail to deal with complex reliance among subtasks, which results in mistreatment in the intertraculity of Inra- and the internal operating system.

The Mais, Institute of Automation, Chinese Academy of Science, Chinese, Technical School, University of China Academy of Science, Alia Group, Science School and Technology, Shanghaithy University Appreciates Agent frame Dealing with complex PC conditions by using three new projects. First, A valid comprehension module Enhances good communication with issues and definitions of practical objects through trees, while using the understanding of MLLM-conducted in front of the text. Second, Hierarchical Multi-Agent collective partnership It uses the process of decent decisions (verb-subtask) where manager mockroes orders are structured and manage history, as well as the progress agent following the steps in view and progress. Third, Thinking based on making strong decisions Provisional Agency examining the accuracy of the murder and provides a high-quality basis for all four agents involving all four agents involving all four agents.

PC-Agent's Architecture Addresses in a systematic addresses when an agent ρ determines users' instructions in, visualization o, and history The complex work movement, the use of the PC-agent is used for the Alent Multi-Alent Levelation: Manager Holds Instructions to organized subtasks and regulates reliance; Progress agent tracks the progress of the operations within the underground; And decision agent does step-by-step items based on environmental identification and progress details. This Hierarchical incident is effective in making decisions about violating complex tasks into a clearer form.

The test results indicate higher PC-agent function compared to other single and many ways. GPT-4O, Gemini-2.0, claude3.5, claude.5. with issues of handling to depend on. Fighting good works such as planning of text words or access to the relevant data words in Excel, and often fails to use information from previous subtask details. In contrast, the most PC-agent is supported every previous methods, passing the UFO by 44% and agents with the effective functioning of cooperation.

This study is introduced The frame of agent, Important development in handling the complex tasks of PC-based Passion through the new new. Active comprehension module provides a refined understanding and performance skills, which makes direct partnerships with GUI Elements and the text. The cooperation of the Hierarchical Multi-agent management is making decisions for all orders, subtask, as well as the actions levels, during the consideration of motivational decisions that allocate acquisitions and real repairs. Verification of a newly created PC-evalb is created, complex orders guarantees the highest PC-agent performance compared to previous methods, which indicates its complexity of the complex operations of PC production conditions.

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