# meta-reflection

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.