GraphSkillAA introduces an attribution-guided skill-graph framework that organizes external skills into addressable nodes and typed edges, enabling precise selection, execution, failure attribution, and targeted updates. The system uses a Local Gate to retest affected examples and a Big Gate to evaluate the entire updated graph, rolling back any rejected changes. Experiments with GPT‑5.6‑sol show high performance on SearchQA, LiveMath, and DocVQA, demonstrating that object-level attribution and graph-scoped validation make skill graphs locally optimizable, testable, and reversible.
By Ziqiao Shang, Ling-Yue Ge, Lan-Zhe Guo
GraphSkillEvo introduces a graph-structured representation for agent skills, where each node encodes an execution step and edges capture context-dependent transitions. This structure offers clearer workflow guidance and reduces redundancy compared to unstructured natural-language skills. The authors then present a population-based evolutionary optimization framework that explores this structured skill space, achieving higher accuracy than the baseline SkillOpt across five agent benchmarks.
By Rui Sun, Zhi Zheng, Zhenkun Wang, Zhichao Lu
arXiv:2606. 01311v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on reusable external skills to solve long-horizon interactive tasks.
By Zhuoyun Yu, Xin Xie, Wuguannan Yao, Chenxi Wang, Lei Liang, Xiang Qi, Shumin Deng
CaSKG introduces a counterfactual‑causal skill graph framework that calibrates procedural relations before retrieval, building a high‑recall directed candidate graph from semantic, lexical, input/output, and structural evidence and refining it with repair evidence and optional LLM judgment. The framework applies direction‑conditioned textual counterfactual probes—removing, substituting, and reordering skill pairs—to aggregate evidence with Bayesian smoothing, producing a state‑filtered weighted graph for task‑conditioned expansion. Evaluated across six LLM backbones on ALFWorld and ScienceWorld, CaSKG outperforms existing Graph‑of‑Skills methods, improving macro‑average scores and reducing mean environment steps while preserving essential skill dependencies.
By Zhiyuan Li, Linyuan Gao, Xuechun Ding, Hongwei Chen, Yuan Wu, Yi Chang
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: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