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IntentCUA: Learning Intent-level Representations for Skill Abstraction and Multi-Agent Planning in Computer-Use Agents
arXiv – CS AI|Seoyoung Lee, Seobin Yoon, Seongbeen Lee, Yoojung Chun, Dayoung Park, Doyeon Kim, Joo Yong Sim||17 views
🤖AI Summary
Researchers introduced IntentCUA, a multi-agent framework for computer automation that achieved 74.83% task success rate through intent-aligned planning and memory systems. The system uses coordinated agents (Planner, Plan-Optimizer, and Critic) to reduce error accumulation and improve efficiency in long-horizon desktop automation tasks.
Key Takeaways
- →IntentCUA framework achieved 74.83% task success rate with 0.91 step efficiency ratio, outperforming existing RL-based and trajectory methods
- →The system uses three coordinated agents sharing memory to abstract interaction traces into reusable skills and intent representations
- →Multi-view intent abstraction and shared plan memory jointly improve execution stability in desktop automation
- →The approach reduces redundant re-planning and mitigates error propagation across desktop applications
- →Research demonstrates that system-level intent abstraction is key to reliable automation in dynamic environments
#ai-agents#computer-automation#multi-agent-systems#machine-learning#desktop-automation#intent-planning#ai-research#arxiv
Read Original →via arXiv – CS AI
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