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Skill Retrieval Augmentation for Agentic AI

A paper proposing a paradigm for dynamically retrieving and incorporating skills in large language models.

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Skill Retrieval Augmentation for Agentic AI

By Weihang Su, Jianming Long, Qingyao Ai, Qiaozhi He, Yichen Tang, Changyue Wang, Yiteng Tu, Yingbo WangarXiv
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The authors introduce Skill Retrieval Augmentation (SRA), a method for agents to retrieve relevant skills from external corpora on demand. They construct a benchmark, SRA-Bench, to evaluate the full SRA pipeline.

The paper shows that retrieval-based skill augmentation can improve agent performance and highlights the need for more efficient skill incorporation.

Abstract

The authors introduce Skill Retrieval Augmentation (SRA), a method for agents to retrieve relevant skills from external corpora on demand. They construct a benchmark, SRA-Bench, to evaluate the full SRA pipeline. The paper shows that retrieval-based skill augmentation can improve agent performance and highlights the need for more efficient skill incorporation.

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skill retrievalaugmentation paradigmlarge language modelsagent performance improvementbenchmark evaluationAI AgentsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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