# Evolutionary Optimization

Evolutionary optimization for production AI systems no longer picks one candidate from the search frontier and rewrites it from its failures alone. CoCoEvolve has an agentic mutator retrieve evidence from the entire evolutionary history and probe the live system before proposing changes. On DABStep it reaches 92.8% held-out hard accuracy, against 78.4% for Meta-Harness and 30.4% for GEPA under matched conditions; on data-eng-bench it lifts test-set solve rate from 59.1% to 77.4%, past the 73.6% best baseline.

Evolutionary optimization for production AI systems no longer picks one candidate from the search frontier and rewrites it from its failures alone. CoCoEvolve has an agentic mutator retrieve evidence from the entire evolutionary history and probe the live system before proposing changes. On DABStep it reaches 92.8% held-out hard accuracy, against 78.4% for Meta-Harness and 30.4% for GEPA under matched conditions; on data-eng-bench it lifts test-set solve rate from 59.1% to 77.4%, past the 73.6% best baseline.

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.