title: PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex
    Task Automation on PC
summary: In the field of MLLM-based GUI agents, compared to smartphones, the PC
  scenario not only features a more complex interactive environment, but also
  involves more intricate intra- and inter-app workflows. To address these
  issues, we propose a hierarchical agent framework named PC-Agent. Specifically,
  from the perception perspective, we devise an Active Perception Module (APM) to
  overcome the inadequate abilities of current MLLMs in perceiving screenshot
  content. From the decision-making perspective, to handle complex user
  instructions and interdependent subtasks more effectively, we propose a
  hierarchical multi-agent collaboration architecture that decomposes
  decision-making processes into Instruction-Subtask-Action levels. Within this
  architecture, three agents (i.e., Manager, Progress and Decision) are set up
  for instruction decomposition, progress tracking and step-by-step
  decision-making respectively. Additionally, a Reflection agent is adopted to
  enable timely bottom-up error feedback and adjustment. We also introduce a new
  benchmark PC-Eval with 25 real-world complex instructions. Empirical results on
  PC-Eval show that our PC-Agent achieves a 32% absolute improvement of task
  success rate over previous state-of-the-art methods. The code will be publicly
  available.
paper_authors._id: 67b7f5587f4d732dc469270e
paper_authors.name: Haowei Liu
paper_authors._id: 67b7f5587f4d732dc469270f
paper_authors.name: Xi Zhang
paper_authors._id: 67b7f5587f4d732dc4692710
paper_authors.name: Haiyang Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-21T14:58:29.601Z
paper_authors._id: 66e8d7d2483df532fd364913
paper_authors.avatarUrl: /avatars/300aea4b8c571b2aeac629de58281444.svg
paper_authors.fullname: Haiyang Xu
paper_authors.type: user
paper_authors.user: msxxx
paper_authors._id: 67b7f5587f4d732dc4692711
paper_authors.name: Yuyang Wanyan
paper_authors._id: 67b7f5587f4d732dc4692712
paper_authors.name: Junyang Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-21T14:58:16.343Z
paper_authors._id: 6438f6415aa69077ffb16942
paper_authors.avatarUrl: /avatars/c83dbd3e10e88db97c2a86092bad5917.svg
paper_authors.fullname: Junyang Wang
paper_authors.type: user
paper_authors.user: junyangwang0410
paper_authors._id: 67b7f5587f4d732dc4692713
paper_authors.name: Ming Yan
paper_authors._id: 67b7f5587f4d732dc4692714
paper_authors.name: Ji Zhang
paper_authors._id: 67b7f5587f4d732dc4692715
paper_authors.name: Chunfeng Yuan
paper_authors._id: 67b7f5587f4d732dc4692716
paper_authors.name: Changsheng Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-21T14:57:51.040Z
paper_authors._id: 6222e721271a284f976f43d8
paper_authors.avatarUrl: /avatars/36ce9f16de6f4ae6ea0968c49207f191.svg
paper_authors.fullname: ChangshengXu
paper_authors.type: user
paper_authors.user: ChangshengXu
paper_authors._id: 67b7f5587f4d732dc4692717
paper_authors.name: Weiming Hu
paper_authors._id: 67b7f5587f4d732dc4692718
paper_authors.name: Fei Huang
paper_title: PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex
    Task Automation on PC
paper_summary: In the field of MLLM-based GUI agents, compared to smartphones, the PC
  scenario not only features a more complex interactive environment, but also
  involves more intricate intra- and inter-app workflows. To address these
  issues, we propose a hierarchical agent framework named PC-Agent. Specifically,
  from the perception perspective, we devise an Active Perception Module (APM) to
  overcome the inadequate abilities of current MLLMs in perceiving screenshot
  content. From the decision-making perspective, to handle complex user
  instructions and interdependent subtasks more effectively, we propose a
  hierarchical multi-agent collaboration architecture that decomposes
  decision-making processes into Instruction-Subtask-Action levels. Within this
  architecture, three agents (i.e., Manager, Progress and Decision) are set up
  for instruction decomposition, progress tracking and step-by-step
  decision-making respectively. Additionally, a Reflection agent is adopted to
  enable timely bottom-up error feedback and adjustment. We also introduce a new
  benchmark PC-Eval with 25 real-world complex instructions. Empirical results on
  PC-Eval show that our PC-Agent achieves a 32% absolute improvement of task
  success rate over previous state-of-the-art methods. The code will be publicly
  available.
paper_ai_keywords: Active Perception Module (APM) hierarchical multi-agent collaboration architecture Instruction-Subtask-Action levels hierarchical agent framework Manager agent Progress agent Decision agent Reflection agent PC-Eval benchmark PC-Agent PC-Eval