Learning Meta-Skills for Agent Harness Design in Test-Time AI4AI
Agent performance depends on both reasoning ability and the environment in which it acts. We study test-time AI-for-AI, asking how a Builder can learn to construct better execution environments for a Target while both models' weights remain fixed. To make the Builder's experience reusable, we introduce Meta-Skill: principles specifying when support is needed and what resources
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Agent performance depends on both reasoning ability and the environment in which it acts. We study test-time AI-for-AI, asking how a Builder can learn to construct better execution environments for a Target while both models' weights remain fixed. To make the Builder's experience reusable, we introduce Meta-Skill: principles specifying when support is needed and what resources
This paper introduces a novel approach to improving agent performance by learning 'meta-skills' for environment construction, rather than directly modifying agent weights. This paradigm, known as test-time AI-for-AI, offers a powerful way to enhance agent capabilities and reusability, which is crucial for developing robust and adaptable AI systems in complex, dynamic environments.