SkillAA (Skill Abductive Attribution) is a structured skill‑optimization framework designed for frozen language models. It represents skill applicability, execution, and composition in a unified graph, enabling skill selection, attribution‑guided repair, and update validation within the same structure. By contrasting successful and failed executions, SkillAA routes candidate repairs to specific graph objects, updates only the selected local structure, and uses Local and Big Gates to screen candidate changes before commitment, achieving high performance on SearchQA, LiveMath, and DocVQA.
By Ziqiao Shang, Ling-Yue Ge, Lan-Zhe Guo
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
The paper investigates a failure mode in Graph-JEPA, a joint‑embedding predictive model trained on a large scientific‑reasoning graph. Despite achieving high linear‑probe accuracy and effective rank, the learned representation contains almost no usable instance information, as shown by retrieval metrics. The authors diagnose the issue to variance allocation in the objective, propose a repair that restores near‑perfect information recovery, and demonstrate that the problem persists even after repair, highlighting limitations in the evaluation metrics used.
By Gollam Rabby, S\"oren Auer
arXiv:2608. 07639v1 Announce Type: cross Abstract: Agent Skills provide reusable capabilities to LLM agents.
By Chaofan Meng, Yuhang Zheng, Yingnan Zhou, Sihan Xu
arXiv:2606. 03056v1 Announce Type: new Abstract: As LLM agents adopt large skill libraries, selecting the right subset becomes a structural problem rather than a similarity-matching one: skills depend on, conflict with, specialize, or duplicate one another, a structure invisible to both full enumeration and embedding similarity.
By Tong Bai, Zhenglin Wan, Pengfei Zhou, Xingrui Yu, Wangbo Zhao, Yang You, Ivor W. Tsang
arXiv:2607. 10113v1 Announce Type: new Abstract: Large language model agents increasingly store reusable procedures outside the model.
By Yubo Li