Current AI systems can learn new rules through exploration, but performance is inconsistent and fragile—gains from exploration can reverse with continued interaction, suggesting exploration capabilities need significant improvement.
ExplorationBench is a benchmark that tests whether AI systems can genuinely explore and discover new rules in unfamiliar environments, rather than just recalling training data. It uses two 'Alien Worlds' with executable rules that differ from real-world knowledge, forcing systems to experiment, form hypotheses, and learn through interaction rather than memorization.