Evolver Reuses Agent Trajectories to Improve Task Success
The key implication is that harness-level memory can outperform parameter tuning: agents get better purely by reusing learned procedures, making this applicable to any existing model deployment. Practitioners building coding agents or evaluation pipelines can adopt the Gene-asset pattern to cut token costs. Terminal-Bench 2.1 and SWE-bench Multilingual results give concrete baselines for comparing against their own harness setups.
