Rui Sun

The work represents LLM agent skills as graph-structured natural-language artifacts, where each node is an execution step with its operational guidance and directed edges encode context-dependent transitions between steps, and builds GraphSkillEvo, a population-based evolutionary framework with global-guidance mutation, graph-structure mutation, global-guidance crossover, and graph-structure crossover that searches this structured skill space, outperforming the skill-optimization baseline SkillOpt on average across five agent benchmarks, two LLMs, and two execution settings (+4.01% on GPT-5.4-nano and +1.76% on GPT-5.4) while consuming fewer total optimization tokens.

The work represents LLM agent skills as graph-structured natural-language artifacts, where each node is an execution step with its operational guidance and directed edges encode context-dependent transitions between steps, and builds GraphSkillEvo, a population-based evolutionary framework with global-guidance mutation, graph-structure mutation, global-guidance crossover, and graph-structure crossover that searches this structured skill space, outperforming the skill-optimization baseline SkillOpt on average across five agent benchmarks, two LLMs, and two execution settings (+4.01% on GPT-5.4-nano and +1.76% on GPT-5.4) while consuming fewer total optimization tokens.