English(EN) Beyond Gain: Measuring Skill Internalization for Agent Skill Retirement

新方法可准确衡量人工智能智能体技能内化程度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 04:00

研究人员开发了一种新方法，可以准确衡量人工智能智能体何时内化了技能，从而能够更有效地从技能中心退役冗余技能。仅依赖性能提升的传统方法并不可靠，因为它们可能错误地识别技能已被内化或未被发现。所提出的方法包括保留一项技能，评估智能体在综合任务上的无辅助性能，并通过检查其他模型是否从该技能中受益来验证此分数。与现有的基于增益的规则相比，该方法在SkillsBench数据集上显示出更高的可靠性和准确性。 AI

影响这种衡量技能内化程度的新方法可以通过减少冗余技能和改进性能跟踪，从而实现更高效、更有效的人工智能智能体开发。

排序理由该集群包含一篇详细介绍评估人工智能智能体能力的新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yanze Wang, Pengfei Yao, Tianyi Sun, Hongda Chen, Yunyun Han, Jun Sun, Chuanrui Hu, Yafeng Deng · 2026-10-26 04:00

超越收益：衡量技能内化以实现智能体技能退役

arXiv:2610.32322v1 Announce Type: new Abstract: LLM agents retrieve skills from public hubs that hold up to about 100,000 entries and serve many models. As models improve, a hub must find the skills a given model has internalized, meaning it can do without them, and retire them f…

本文如何被排名

报道来源 [1]

超越收益：衡量技能内化以实现智能体技能退役

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