测试时AI4AI元技能学习研究
Learning Meta-Skills for Agent Harness Design in Test-Time AI4AI
这篇论文教你如何让AI模型学习如何为其他AI模型构建更好的执行环境，提升性能近9个百分点。
研究提出元技能概念，让Builder模型从Target模型的执行反馈中学习构建更好执行环境的原理。在Harness-Bench和NewtonBench基准测试中，完整元技能库比无技能构建提升8.95个百分点，比直接交付技能库提升12.02个百分点。同一模型同时担任两个角色时，系统展现出通过学习构建更好环境实现自我改进的潜力。
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 to provide. The Builder learns these principles from Target's execution feedback on the development set, then uses the frozen skill bank to construct harnesses for unseen tasks. Across Harness-Bench and NewtonBench, full-bank meta-skills improve macro-average performance by 8.95 percentage points over no-skill construction, and 12.02 points over direct delivery of the same bank to the Target. These results highlight the value of translating experience into executable support. Gains when the same model serves both roles further suggest a path to system level self-improvement through learning to build better environments.
