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MAGE framework uses user manuals to improve AI agent performance on new software

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MAGE, a framework designed to enhance the capabilities of computer use agents (CUAs) by leveraging user manuals. MAGE improves agent performance on new software by using manuals for both execution and training, reducing the need for human-annotated data. The system retrieves relevant manual content to guide agent actions and adapts the policy through interaction with the environment. For training, MAGE converts manuals into executable task-plan pairs, which then guide the agent's learning process. Experiments across six applications show MAGE's effectiveness in increasing task success rates compared to existing baselines. AI

IMPACT This framework could significantly reduce the cost and effort required to train AI agents for new software applications.

RANK_REASON The cluster contains an academic paper detailing a new framework for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

MAGE framework uses user manuals to improve AI agent performance on new software

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The cluster contains an academic paper detailing a new framework for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Chulhoon Yoon, Kyungri Park, Jinyeong Yun, Daeyun Jeong, Sukmin Yun, Woohwan Jung ·

    Adaptation of Computer Use Agents to New Software with User Manuals

    arXiv:2610.23012v1 Announce Type: new Abstract: Recent advances in multimodal foundation models have significantly improved the ability to operate real-world software through graphical user interfaces (GUIs) using visual observations and natural language instructions. However, ma…

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