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

Abstract: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 \textsc{StatefulDiscovery}, a discovery framework that externalizes investigation state and uses it to coordinate frontier selection, evidence acquisition, and claim adjudication. We evaluate \textsc{StatefulDiscovery} across 40 real-data discovery tasks. Compared with several baselines, \textsc{StatefulDiscovery} 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 \href{this https URL}{this https URL}.

Artificial Intelligence (cs.AI)

arXiv:2606.11851 [cs.AI]

(or arXiv:2606.11851v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.11851 Focus to learn more arXiv-issued DOI via DataCite

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