ExplorationBench: Measuring AI Systems' Exploration in Verifiable Alien Worlds
Scientific discovery begins where known problems end. There, AI systems must engage in exploration: framing hypotheses, designing experiments, and iterating on the results. However, evaluating this ability is difficult: (1) how to verify whether a genuinely new hypothesis holds, and (2) how to determine whether a system has discovered it through exploration or merely recalled r
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Scientific discovery begins where known problems end. There, AI systems must engage in exploration: framing hypotheses, designing experiments, and iterating on the results. However, evaluating this ability is difficult: (1) how to verify whether a genuinely new hypothesis holds, and (2) how to determine whether a system has discovered it through exploration or merely recalled r
This work introduces a novel benchmark for evaluating AI systems' exploratory capabilities, moving beyond recall-based tasks. For ML engineers, understanding such benchmarks is crucial for developing and assessing AI agents that can truly innovate and discover in unknown environments.