| title | GraphSkillEvo: Evolutionary Optimization of Graph-Structured Agent Skills |
|---|---|
| authors | Rui Sun, Zhi Zheng, Zhenkun Wang, Zhichao Lu |
| date | 2026-09-18 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.21749 |
| upvotes | 18 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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 https://github.com/ruisun7/GraphSkillEvo. |
cite
@misc{sun2026graphskillevo,
title={GraphSkillEvo: Evolutionary Optimization of Graph-Structured Agent Skills},
author={Rui Sun and Zhi Zheng and Zhenkun Wang and Zhichao Lu},
year={2026},
eprint={2609.21749},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.21749}
}