MultiEvoSkill is a skill‑optimization framework that transforms skill documents into typed graphs, enabling explicit procedural topology and parallel branches. It employs a heterogeneous agent system to generate revision suggestions based on diverse experiences, and a structure‑aware debate mechanism to resolve edits while respecting dependencies and importance constraints. Experiments show that this approach outperforms state‑of‑the‑art methods and improves cross‑model generalization of skill artifacts.
By Kounianhua Du, Jianxing Liu, Ruoshui Shan, Yucheng Zhang, Ke Li, Xing Sun, Weinan Zhang
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
SkillGraph introduces a skill library framework that models reusable skills as nodes in a directed graph, with typed edges representing prerequisite, enhancement, and co-occurrence relationships. When presented with a new task, the system retrieves an ordered subgraph of relevant skills, guiding multi-step decision making. The graph is continuously refined through agent trajectories and reinforcement learning, enabling simultaneous improvement of the skill library and the agent policy, and achieving state‑of‑the‑art results on ALFWorld, WebShop, and several search‑augmented QA tasks.
By Xiaoyuan Li, Moxin Li, Keqin Bao, Yubo Ma, Wenjie Wang, Dayiheng Liu, Fuli Feng
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
arXiv:2606. 26578v1 Announce Type: new Abstract: Automating optimization modeling from natural language with large language models (LLMs) faces two key challenges.
By Qingcan Kang, Mingyang Liu, Xiaojin Fu, Shixiong Kai, Tao Zhong, Mingxuan Yuan
arXiv:2608. 15071v1 Announce Type: new Abstract: Learning from experience is critical for developing capable, self-improving large language model (LLM) agents.
By Tianxin Wei, Zhan Shi, Minhua Lin, Bing He, Zewen Liu, Yisi Sang, Yuanchen Bei, Xuying Ning, Jiaru Zou, Ting-Wei Li, Xiao Lin, Yanjun Zhao, Chi Wang, Benoit Dumoulin, Dakuo Wang, Jingrui He, Hanqing Lu
arXiv:2606. 16774v1 Announce Type: new Abstract: Equipping Large Language Model (LLM) agents with effective skills is crucial for solving complex tasks in real-world systems like OpenClaw.
By Tianyi Lin, Chuanyu Sun, Jingyi Zhang, Changxu Wei, Huanjin Yao, Shunyu Liu, Xikun Zhang, Liu Liu, Jiaxing Huang
The paper introduces Procedural Graphs, a framework that structures procedural knowledge for large language model agents as (procedure, relation, procedure) triplets, analogous to knowledge graphs for factual data. At each decision point, a guidance model uses the local subgraph to bias the agent’s next action, while an LLM refiner self‑evolves the graph by comparing failed and successful trajectories, editing its topology to improve performance. Experiments across various datasets, tasks, and LLMs show that Procedural Graphs consistently outperform memory‑based baselines, and the self‑evolution mechanism further enhances results without manual engineering.
By Yuxing Lu, Yicheng Chen, Shanchan Wu, Sercan \"{O}. Ar{\i}k
The paper introduces the Procedural Graph, a framework that structures procedural knowledge into (procedure, relation, procedure) triplets to guide large language model agents in planning and tool usage. At each decision point, a guidance model uses the local subgraph to bias the agent’s next action, while an LLM refiner self‑evolves the graph by editing its topology based on successful versus failed trajectories. Experiments across datasets and LLMs show that Procedural Graphs consistently outperform memory‑based baselines, and the self‑evolution mechanism further improves performance without manual engineering.
arXiv:2609.08944v1 Announce Type: new
Abstract: Agent skills provide a lightweight way to equip frozen language-model agents with domain knowledge and procedural guidance, yet obtaining high-quality...
By Gaoyuan Li, Meihao Fan, Yizhe Liu, Shaolei Zhang, Ju Fan, Siyi Wang, Jiaheng Hou, Xudong Weng, Honghan Tian, Zang Li
SkillSpec is a two‑phase framework for evolving natural‑language skills in large language model agents. The first phase, consensus‑gated evolution, generates candidate skills from complementary editing intents and commits updates only when paired evaluations reach consensus on overall improvement and non‑negative aggregate gain. The second phase, representation specialization, uses signals from the optimization trajectory to choose an appropriate flat, graph, or hybrid structure for the skill, improving success rates by an average of 6.89% over SkillOpt across six benchmarks and three target language models.
By Huancheng Chen, Xiaodi Sun, Zhaoqiong Huang, Shenyang Huang Shreya Singhal, Jingwen Lu
Rep2Skill introduces a representation-guided framework that enables large language model agents to self-evolve their textual skills by analyzing internal representation trajectories from agent rollouts. The method identifies execution turns that deviate from successful dynamics and uses these signals, together with execution contexts, as actionable feedback for targeted skill revision. Experiments with two open-source LLMs across two agent environments demonstrate that Rep2Skill consistently outperforms purely text-based approaches, showing that incorporating internal representations can enhance agent self-improvement.
By Kaixing Zhang, Changming Li, Yingdong Shi, Zheng Zhang, Kaitao Song, Wenjie Shi, Jingang Wang, Kan Ren