Epistemic Control for Open-world AI Scientists

AI-scientist systems increasingly automate hypothesis generation, experimentation, and analysis. Yet predictive success alone does not determine whether a scientific explanation should be retained, revised, or replaced. We study epistemic control: the decisions connecting evidence acquisition to accepted explanations and scientific claims. We organise the problem around representation revision, abductive hypothesis generation, and the control of inquiry and belief revision, with six operational criteria for their coordination. We investigate selected criteria using a literature-conditioned discovery prototype and separate controlled policy studies. The prototype acquires variables from a predefined catalogue and generates expressions within a fixed executable language; the policy studies control candidates, evidence access, and revision opportunities. The results show that evidence-selection and revision policies affect inquiry efficiency and accepted conclusions, that a genuine cause can look redundant for prediction, and that recovering the right variables need not recover the generating mechanism. These findings identify limitations of predictive criteria for scientific decisions and provide controlled tests of components of epistemic control. Autonomous diagnosis and revision of an inadequate scientific representation remains the broader challenge.