AIMS: an AI experimentalist turns uncertainty into quantum matter discovery
Most AI agents act only after scientists have defined the task. Discovery is harder under practical uncertainties: the probe may not be where it is expected, the signal may occupy only a small region of a disordered sample, and the evidence may not distinguish among competing explanations. Here we show that an AI agent can decide what evidence an uncertain experiment needs next, and act on it. Beyond automation, AIMS, an uncertainty-aware experimentalist for cryogenic microwave impedance microscopy, quantifies uncertainty where it originates, in perception, sampling, and interpretation, and converts each into its own corrective action rather than a single confidence score. Given only an open objective, AIMS relocated a probe lost during cooldown while flagging its own unreliable estimates, mapped twist angle disorder to locate the strongest correlated states in twisted bilayer MoSe$_2$, and uncovered a paradox: the half-filled stripe that classical theory predicts should melt first survived longest. Distinguishing an incomplete model from a wrong mechanism, AIMS commissioned a beyond-mean-field calculation and an independent structural measurement as the decisive tests, revising its interpretation as each arrived: quantum motion reverses the classical hierarchy, stabilizing the half-filled stripe while destabilizing its neighbors. These uncertainty-to-action loops are generic to scanning probe experiments, and AIMS turns uncertainty from an obstacle into a driver of discovery.