智能体
arxiv.org ·
GraphSkillEvo：图结构智能体技能的进化优化
该论文提出将 LLM 智能体的技能表示为图结构自然语言工件：节点为执行步骤及其操作指引，有向边编码步骤间的上下文相关转移，以提供明确的工作流级指导并降低冗余。在此基础上提出 GraphSkillEvo，一种基于种群的进化优化框架，通过变异与交叉算子维护多个候选技能并组合有效组件，以比纯 LLM 迭代自精炼更广地探索结构化技能空间。在五个 agent 基准上，该方法一致优于 SkillOpt 基线，GPT-5.4-nano 平均准确率提升 4.01%，GPT-5.4 提升 1.76%。代码已公开。
AI推荐理由 arXiv 预印本，作者与摘要信息完整，方法描述具体（图结构技能表示 + 基于种群的进化优化，含 mutation/crossover），并给出在五个 agent benchmark 上相对 SkillOpt 的平均准确率提升（GPT-5.4-nano +4.01%、GPT-5.4 +1.76%），属可验证的研究贡献。局限：仅为预印本，未经同行评审，实验细节与复现性需看全文；摘要未说明基准规模、成本与失败案例。
78
正文
Computer Science > Machine Learning
arXiv:2609.21749 (cs)
[Submitted on 18 Sep 2026]
Title:GraphSkillEvo: Evolutionary Optimization of Graph-Structured Agent Skills
Authors:Rui Sun, Zhi Zheng, Zhenkun Wang, Zhichao Lu
View a PDF of the paper titled GraphSkillEvo: Evolutionary Optimization of Graph-Structured Agent Skills, by Rui Sun and 3 other authors
View PDF
HTML (experimental)
Abstract:Skills can improve the performance of Large Language Model (LLM) agents by providing task-specific procedural guidance, while skill optimization further improves their effectiveness through iterative refinement. However, existing skill optimization methods typically represent skills as unstructured natural-language instructions, creating two key challenges: 1) Unstructured skills often lack explicit workflow-level guidance and contain substantial redundancy, making them difficult for LLMs to execute; 2) the vast search space of unconstrained natural-language skills makes skill optimization ineffective. To address these challenges, we propose representing skills as graph-structured natural-language artifacts. In graph-structured skills, each node represents an execution step together with its operational guidance, while directed edges encode context-dependent transitions between steps. Compared to unstructured skills, graph-structured skills can provide clear workflow-level guidance. Moreover, the proposed graph-structured skill can also facilitate skill optimization. Building on this structured representation, we introduce GraphSkillEvo, a population-based evolutionary optimization framework with mutation and crossover operators for graph-structured skills. By maintaining multiple candidate skills and combining effective components, GraphSkillEvo enables broader and more comprehensive exploration of the structured skill space than purely LLM-based iterative self-refinement. Extensive experiments across five agent benchmarks demonstrate that GraphSkillEvo consistently outperforms the strong skill optimization baseline SkillOpt, improving average accuracy by 4.01% on GPT-5.4-nano and 1.76% on GPT-5.4. Our code is available at this https URL.
Subjects:
Machine Learning (cs.LG)
Cite as:
arXiv:2609.21749 [cs.LG]
(or
arXiv:2609.21749v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.21749
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhi Zheng [view email]
[v1]
Fri, 18 Sep 2026 13:24:33 UTC (454 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled GraphSkillEvo: Evolutionary Optimization of Graph-Structured Agent Skills, by Rui Sun and 3 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.LG
new
|
recent
| 2026-09
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
export BibTeX citation
Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
IArxiv recommender toggle
IArxiv Recommender
(What is IArxiv?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? |
Disable MathJax (What is MathJax?)