GraphSkillEvo: Evolutionary Optimization of Graph-Structured Agent Skills
GraphSkillEvo evolves graph-structured LLM agent skills with mutation and crossover, beating SkillOpt by 4.01% on GPT-5.4-nano.
The paper represents agent skills as graph-structured natural-language artifacts where nodes are execution steps with operational guidance and directed edges encode context-dependent transitions. GraphSkillEvo applies population-based evolutionary optimization with mutation and crossover operators over these graphs, exploring the structured skill space more broadly than iterative LLM self-refinement. Across five agent benchmarks it improves average accuracy by 4.01% on GPT-5.4-nano and 1.76% on GPT-5.4 over the SkillOpt baseline.
- Skills modeled as graphs: nodes are steps, edges are contextual transitions
- Population-based evolution with mutation and crossover operators
- Improves accuracy 4.01% (GPT-5.4-nano) and 1.76% (GPT-5.4) over SkillOpt
From the original article
1 min read at huggingface.co
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.
By Rui Sun, Zhi Zheng, Zhenkun Wang · huggingface.co
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