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This paper introduces Opal (Opportunity-aware Policy Authorization for Laboratories), a framework that certifies whether adaptive experimentation should be enabled by precommitting to non-trivial adaptation, controlled target risk, and positive executed value after cost. It establishes an impossibility boundary and demonstrates the method on a Cell Painting dataset, achieving risk control and positive value.
本文提出了一种两步法，用于优化自主实验室中的资源利用率，该方法使用约束规划进行调度，并利用状态依赖关系实现稳健执行，并在金属有机框架合成平台上进行了演示。