The paper introduces Muse-Autoskill, a framework for AI agents to autonomously create, refine, and reuse skills from experience without manual intervention. It uses a skill library and memory system to improve performance on complex tasks by identifying common patterns and reusing successful strategies. Experiments show Muse-Autoskill outperforms baseline methods across various benchmarks.
The paper introduces Muse-Autoskill, a framework for AI agents to autonomously create, refine, and reuse skills from experience without manual intervention. It uses a skill library and memory system to improve performance on complex tasks by identifying common patterns and reusing successful strategies. Experiments show Muse-Autoskill outperforms baseline methods across various benchmarks.
The paper introduces AutoScientists, a framework where teams of AI agents autonomously organize to design and conduct scientific experiments. The system enables self-organization of agent roles to handle various experimental tasks without human intervention, potentially accelerating the research process.
The paper introduces Muse-Autoskill, a framework for AI agents to autonomously create, refine, and reuse skills from experience without manual intervention. It uses a skill library and memory system to improve performance on complex tasks by identifying common patterns and reusing successful strategies. Experiments show Muse-Autoskill outperforms baseline methods across various benchmarks.
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