Automated Scientific Discovery via Causal Falsification and Formal Multi-Agent Verification

Scientific AI systems can generate hypotheses and explanations, but many opti-mize plausibility more than refutability. This paper presents a falsification-drivenmulti-agent framework in which specialized agents propose hypotheses, build causalmodels, design adversarial tests, and verify formal claims. The architecture com-bines hypothesis generation, causal reasoning, falsification, formal verification, andpersistent orchestration through a shared memory state that records assumptions,counterexamples, interventions, and proof obligations. By treating failed predictionsand invalid proof attempts as useful learning signals, the framework shifts discoveryfrom fluent claim production toward disciplined claim survival. On simulated dis-covery tasks, the full system improves verified discovery rate from 0.42 to 0.76 andreduces false-positive hypothesis retention from 0.31 to 0.08. Scaling experimentsshow a peak discovery quality factor of 0.83 with eight agents, supporting the prin-ciple that scientific AI should prioritize systematic refutation, causal identifiability,and machine-checkable proof.