arXiv AI

LatentSkill: From In-Context Textual Skills to In-Weight Latent Skills for LLM Agents

arXiv:2606. 06087v1 Announce Type: cross Abstract: Agent systems increasingly use textual skills to encode reusable task procedures, but injecting these skills into the prompt at every step incurs substantial context overhead and exposes skill content as plaintext.

arXiv AI

Jun 16

Skill-to-LoRA: From Using Skills to Learning Behaviors for Token-Efficient LLM Agents

arXiv:2606. 16769v1 Announce Type: new Abstract: Agent skills are commonly distributed as SKILL.

By Tianyi Zhang, Zhonghao Qi

llms agents fine-tuning safety

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

llms agents benchmarks

Hugging Face Trending Papers

Jun 29

Parametric Skills

Since intelligence fundamentally relies on efficient skill acquisition (Chollet, 2019), the ability to leverage skills is critical. For LLMs, skills, manually authored or extracted from task trajectories, are textual recipes encoding mature problem-solving experience and are critical to agentic capabilities.

llms agents fine-tuning

arXiv AI

Jun 16

SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

arXiv:2602. 12670v4 Announce Type: replace Abstract: Agent Skills are structured packages of procedural knowledge that augment large language model (LLM) agents at inference time.

By Xiangyi Li, Yimin Liu, Wenbo Chen, Bingran You, Zonglin Di, Yifeng He, Shenghan Zheng, Kyoung Whan Choe, Jiankai Sun, Shuyi Wang, Chujun Tao, Binxu Li, Xuandong Zhao, Hejia Geng, Xiaojun Wu, Junwei Zhou, Xiaokun Chen, Hanwen Xing, Yubo Li, Qunhong Zeng, Di Wang, Yuanli Wang, Roey Ben Chaim, Penghao Jiang, Haotian Shen, Luyang Kong, Xinyi Liu, Runhui Wang, Xuanqing Liu, Jiachen Li, Xin Lan, Yueqian Lin, Wengao Ye, Junwei He, Songlin Li, Yue Zhang, Yipeng Gao, Yijiang Li, Ze Ma, Liqiang Jing, Tianyu Wang, Kaixin Li, Yiqi Xue, Haoran Lyu, Yizhuo He, Yuchen Tian, Shutong Wu, Bowei Wang, Yixuan Gao, Bo Chen, Litong Liu, Sikai Cheng, Jiajun Bao, Shuaicheng Tong, Shuwen Xu, Terry Yue Zhuo, Tinghan Ye, Qi Qi, Miao Li, Longtai Liao, Zelin Tan, Chang Shi, Xilin Tang, Srinath Tankasala, Boqin Yuan, Yaoyao Qian, Jianhong Tu, Chenguang Wang, Yizhou Sun, Wei Wang, Aaron Taylor, Ziyue Yang, Changkun Guan, Zhikang Dong, Xinyu Zhang, Steven Dillmann, Han-chung Lee, Dawn Song

llms agents benchmarks

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

Jul 29

HiSkill: Empowering LLM Agents with Hierarchical Skill Graphs

arXiv:2607. 25853v1 Announce Type: new Abstract: Skills have become an important abstraction for enabling large language model (LLM) agents to reuse past experience in long-horizon interactive tasks.

By Yu Hao, Jinxuan Cai, Qi Zhang, Yawen Li, Zhiqiang Zhang, Chuan Shi, Cheng Yang

llms agents benchmarks

arXiv AI

Jun 19

SoftSkill: Behavioral Compression for Contextual Adaptation

arXiv:2606. 20333v1 Announce Type: new Abstract: Agent skills are commonly deployed as natural-language Markdown files that encode answer policies, evidence-use habits, and task procedures.

By Xijia Tao, Yihua Teng, Xinyu Fu, Ziru Liu, Kecheng Chen, Yuzhi Zhao, Suiyun Zhang, Rui Liu, Lingpeng Kong

llms agents

arXiv AI

1d ago

SkillCommit: Evolving Agent Skills through Behaviorally Validated Scope Expansion

arXiv:2608. 15165v1 Announce Type: new Abstract: Large language model (LLM) agents can continually improve without parameter updates by converting historical experience into reusable procedural knowledge.

By Yu He, Weikai Yang

llms rag agents

arXiv AI

Jun 17

A Framework for Evaluating Agentic Skills at Scale

arXiv:2606. 17819v1 Announce Type: cross Abstract: Agent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet their cross-domain impact and use across commercial and open-source models remain under-studied, and no reusable methodology exists for evaluating an individual skill.

By Maksim Shaposhnikov, Nicolas Fortuin, Simon Stipcich, Maria I. Gorinova, Amy Heineike, Rob Willoughby

llms agents

arXiv AI

Jun 9

Anything2Skill: Compiling External Knowledge into Reusable Skills for Agents

arXiv:2606. 09316v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables agents to access external knowledge at inference time, but it primarily retrieves fragmented declarative evidence, leaving agents to repeatedly infer task procedures from passages, manuals, examples, logs, or trajectories.

By Qianjun Pan, Yutao Yang, Junsong Li, Jie Zhou, Kai Chen, Xin Li, Qin Chen, Liang He

rag agents

arXiv AI

Jun 3

Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward

arXiv:2602. 12430v4 Announce Type: replace-cross Abstract: The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) are deployed in practice.

By Renjun Xu, Yang Yan

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 11

SkillJuror: Measuring How Agent Skill Organization Changes Runtime Behavior

arXiv:2606. 11543v1 Announce Type: new Abstract: Agent Skills augment large language model (LLM) agents with procedural knowledge at inference time, but current benchmarks rarely distinguish what a Skill says from how it is organized.

By Zhiyu Chen, Zihan Guo, Bo Huang, Bingwei Lu, Jianghao Lin, Yuanjian Zhou, Weinan Zhang

llms agents benchmarks

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