iterative-improvement

AutoLab introduces a benchmark for evaluating long-horizon iterative optimization capabilities of frontier models across diverse domains. Results show that persistence and time awareness are more critical than initial performance, with claude-opus-4.6 demonstrating strong capabilities while many models terminate prematurely.