AI在陌生世界里探索，最强模型也时好时坏
科学发现始于已知问题的尽头，而AI在那里会迷路。这篇论文造了两个「外星世界」——规则可执行、但和常识冲突，AI必须自己摸索规则、设计实验、再解题，靠背答案没用。结果：最强模型能学会并应用陌生规则，但同一模型在不同探索路径上表现忽高忽低，继续探索有时反而让成绩倒退。它不是你明天能用上的东西，但它把「AI能不能真正探索未知」从口号变成了可测量的实验。
📄 原文摘要(英文)
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 related knowledge from pre-training data. To this end, we introduce ExplorationBench, which turns the wicked problem of evaluating scientific exploration into a concrete and tractable framework built on verifiable Alien Worlds: their rules are executable, so every answer can be checked exactly, and they conflict with familiar knowledge, so recall alone cannot solve the tasks. The benchmark contains two sandboxes, AlienCode (31 discovery targets, 70 tasks) and AlienLogic (24 discovery targets, 70 tasks). Each sandbox provides a flawed manual, task-specific environmental feedback, and a dedicated tool-call schema. Systems use these resources to explore the sandbox, then solve held-out tasks. We evaluate 10 AI systems and find that the strongest systems can acquire and apply unfamiliar rules, while performance varies substantially across trajectories and continued exploration can stall or reverse earlier gains. ExplorationBench represents a step towards AI systems that can acquire and apply genuinely new knowledge through exploration in unknown environments.