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About this paper
Tinghe Ding
MOBILEAGENT, developed by Ant Group, enhances LLM-based mobile control agents by integrating human-machine interaction for privacy management and Standard Operating Procedures (SOPs) for improved task comprehension. This approach achieves a new state-of-the-art action success rate of 66.92% on the AitW benchmark without increasing inference costs, while also improving accuracy on internal medical application tasks.
Key insights
- Providing SOPs as an *input* to the LLM significantly improves task comprehension and execution accuracy by reducing conditional entropy, outperforming approaches where plans are generated as *output*.
- Human-machine interaction mechanisms are crucial for managing sensitive information and building user trust in mobile automation agents.
- Performance enhancements in mobile control agents can be achieved without increasing online inference costs by strategically enriching the model's input context.
Summary by alphaXiv.
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