arXiv:2606. 26669v1 Announce Type: new Abstract: Agents often repeatedly solve similar task instances from scratch, leading to unnecessary reasoning cost and long execution traces.
By Zhongxin Guo, Danrui Qi, Hanwen Gu, Peng Cheng, Yongqiang Xiong
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
arXiv:2606. 06893v1 Announce Type: new Abstract: Large language model agents increasingly rely on Skills to encode procedural knowledge, yet high-quality Skills remain costly to hand-write.
By Yuyang Zhang, Xinyuan Han, Xudong Jiang, Run Wang
arXiv:2606. 01139v1 Announce Type: new Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures.
By Yuxuan Liu, Zhaochen Su, Lingyun Xie, Yuhao Zhang, Qing Zong, Jiahe Guo, Zhongwei Xie, Yiyan Ji, Yauwai Yim, Hongyu Luo, Xiyu Ren, Ruan Chenyu, Haoran Li, Yangqiu Song
arXiv:2608. 05604v1 Announce Type: cross Abstract: Large Language Models (LLMs) increasingly act as agents whose procedural knowledge is stored in reusable skill packages and loaded at inference time.
By Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan, Liming Zhu, Wenjie Zhang
arXiv:2607. 20999v1 Announce Type: new Abstract: Agent Skills package reusable procedural knowledge as external artifacts for frozen language-model agents, yet existing optimizers do not jointly resolve where a failure occurs in a workflow, which mechanism caused it, and how relevant knowledge from third-party Skills should be reused locally.
By Zibin Lin, Shengli Zhang, Taotao Wang, Yihan Xia, Deen Ma, Guofu Liao