StatefulDiscovery: Evidence-Calibrated Claim Formation in Open-Ended Scientific Discovery

Open-ended scientific discovery asks agents to move beyond executing analyses for predefined questions. Across multiple rounds of exploration, a discovery agent must decide which phenomena warrant investigation while avoiding overinterpretation, where emerging claims exceed the evidential scope of the analyses. This creates an evidence-calibration problem: the exploration trajectory must be coupled with claim status so that evidence can guide both what to investigate next and what can be claimed. We introduce textscStatefulDiscovery, a discovery framework that externalizes investigation state and uses it to coordinate frontier selection, evidence acquisition, and claim adjudication. We evaluate textscStatefulDiscovery across 40 real-data discovery tasks. Compared with several baselines, textscStatefulDiscovery produces more claims overall judged to be both well-supported and high-value. Ablations indicate distinct roles for structured hypotheses, local adjudication, frontier control and persistent states. Together, these results suggest that explicit discovery state can couple exploration with evidence-calibrated claim formation. Our code is released at hrefhttps://github.com/SUSTech-GenAI/StatefulDiscovery.githttps://github.com/SUSTech-GenAI/StatefulDiscovery.git.