SEER: Learning Scientific Belief Evolution through Falsification-Driven Exploration

Scientific discovery requires explanations that evolve with evidence, whereas most AI-for-Science systems optimize fixed predictive objectives. We introduce Falsification-driven Scientific Belief Learning (FSBL), a paradigm that frames discovery as the evolution of structured, falsifiable scientific beliefs. We instantiate FSBL as SEER (Scientific Exploration through Evidence and Reasoning), a neuro-symbolic system combining neural perception, knowledge-graph-grounded hypothesis generation, Bayesian soft-evidence updating, lifecycle-managed revision, and falsification-driven experiment selection. Each theory specifies a mechanism, quantitative predictions, an executable falsification condition, and a posterior belief. An expected-scientific-value planner selects experiments that discriminate among competing theories and challenge well-supported ones. We evaluate SEER across five closed domains with verifiable ground truth and one exploratory superconductivity problem. Passive coverage fails to distinguish SEER from random search, motivating active metrics for rule recovery, held-out agreement, and interventional validity. SEER recovers all 20 hidden rules in the synthetic world and achieves theory-grounded recovery across real-data domains. Its theories replicate on held-out data, whereas post-hoc rules derived from optimization baselines replicate poorly. Ablations show that removing falsification probes leaves spurious theories supported, while removing the belief system eliminates theory production. On the open problem, SEER sustains multi-generation refinement and identifies descriptor patterns consistent with known physics, with all open-domain claims explicitly scoped to surrogate-level evidence.