arXiv Machine Learning September 2, 2026 By Paimon Goulart, Liang Wu, Kelly Wan, Evangelos E. Papalexakis, Liangjie Hong

Field-Aware Agent Skill Retrieval

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Field-Aware Agent Skill Retrieval explores how keeping the distinct fields of skill documents—such as name, description, and body—separate can improve retrieval performance. By computing sparse and dense similarities for each field independently and combining them with either uniform weights or a small MLP, the authors achieve higher Recall@10 scores on two benchmarks, SkillRet and SRA-Bench. The study shows that the advantage of field-aware representation grows as the skill bank expands, indicating its importance for large-scale lifelong learning agents.

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arXiv Machine Learning

Aug 5

Field Aware Agent Skill Retrieval

arXiv:2608. 02880v1 Announce Type: cross Abstract: As lifelong learning agents accumulate lifelong growing skill banks, retrieving the correct skill becomes an increasingly important bottleneck.

By Paimon Goulart, Liang Wu, Kelly Wan, Evangelos E. Papalexakis, Liangjie Hong

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arXiv AI

3d ago

SkillRet: A Large-Scale Benchmark for Skill Retrieval in LLM Agents

arXiv:2605.05726v2 Announce Type: replace Abstract: As LLM agents are increasingly deployed with large libraries of reusable skills, selecting the right skill for a user request has become a critical...

By Hongcheol Cho, Ryangkyung Kang, Youngeun Kim

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arXiv AI

Jul 22

SkillSight: Seeing Through Shared Descriptions for Accurate Skill Retrieval

arXiv:2607. 18785v1 Announce Type: new Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution.

By Jinying Xiao, Bin Ji, Shasha Li, Xiaodong Liu, Ma Jun, Jiacheng Jie, Chao Wang, Nyima Tashi, Jie Yu

llms agents safety

arXiv AI

Jul 31

SkillSight: Calibrating Generic Content Bias for Skill Retrieval

arXiv:2607. 18785v2 Announce Type: replace Abstract: As large language model agents gain access to increasingly large skill libraries, retrieving the right skill becomes critical to reliable capability selection and execution.

By Jinying Xiao, Bin Li, Xiaopeng Li, Jianling Li, Jiacheng Jie, Xiaodong Liu, Ma Jun, Chao Wang, Nyima Tashi, Jie Yu

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arXiv Machine Learning

Jul 21

SkillRouter: Skill Routing for LLM Agents at Scale

arXiv:2603. 22455v5 Announce Type: replace Abstract: Reusable skills let LLM agents package task-specific procedures, tool affordances, and execution guidance into modular building blocks.

By YanZhao Zheng, ZhenTao Zhang, Chao Ma, YuanQiang Yu, JiHuai Zhu, Yong Wu, Tianze Xu, Baohua Dong, Hangcheng Zhu, Ruohui Huang, Gang Yu

llms rag agents benchmarks

arXiv AI

Jun 9

Skill Retrieval Augmentation for Agentic AI

arXiv:2604. 24594v3 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve into agentic problem solvers, they increasingly rely on external, reusable skills to handle tasks beyond their native parametric capabilities.

By Weihang Su, Jianming Long, Qingyao Ai, Qiaozhi He, Yichen Tang, Changyue Wang, Yiteng Tu, Yingbo Wang, Yiqun Liu

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One email a morning, machine-written

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