PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex
  Task Automation on PC
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.
67b7f5587f4d732dc469270e
Haowei Liu
67b7f5587f4d732dc469270f
Xi Zhang
67b7f5587f4d732dc4692710
Haiyang Xu
admin_assigned
2025-02-21T14:58:29.601Z
66e8d7d2483df532fd364913
/avatars/300aea4b8c571b2aeac629de58281444.svg
Haiyang Xu
user
msxxx
67b7f5587f4d732dc4692711
Yuyang Wanyan
67b7f5587f4d732dc4692712
Junyang Wang
admin_assigned
2025-02-21T14:58:16.343Z
6438f6415aa69077ffb16942
/avatars/c83dbd3e10e88db97c2a86092bad5917.svg
Junyang Wang
user
junyangwang0410
67b7f5587f4d732dc4692713
Ming Yan
67b7f5587f4d732dc4692714
Ji Zhang
67b7f5587f4d732dc4692715
Chunfeng Yuan
67b7f5587f4d732dc4692716
Changsheng Xu
admin_assigned
2025-02-21T14:57:51.040Z
6222e721271a284f976f43d8
/avatars/36ce9f16de6f4ae6ea0968c49207f191.svg
ChangshengXu
user
ChangshengXu
67b7f5587f4d732dc4692717
Weiming Hu
67b7f5587f4d732dc4692718
Fei Huang
PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex
  Task Automation on PC
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.
A hierarchical agent framework called PC-Agent is proposed to improve the performance of MLLM-based GUI agents in complex PC environments through better perception and decision-making processes.
Active Perception Module (APM) hierarchical multi-agent collaboration Instruction-Subtask-Action levels Manager Progress Decision Reflection agent PC-Eval