Adversarial Ensemble Reasoning with Formal Verification: A Methodology for Trustworthy AI-Assisted Scientific Discovery

The proliferation of large language models (LLMs) in scientific research has created a reproducibility crisis in AI-assisted discovery: models hallucinate citations, fabricate data, and —most insidiously — converge on plausible but incorrect consensus through groupthink. We present AegisMind, a neurosymbolic discovery architecture that addresses these failure modes through three compounding mechanisms: (1) structured adversarial multi-model debate across heterogeneous frontier models, (2) diversity-weighted groupthink discounting that mathematically penalises spurious consensus, and (3) Z3 satisfiability modulo theory (SMT) verification of logical consistency in generated hypotheses. The system operates autonomously via a corpus callosum bridge between a rational left-brain API layer and a self-improving right-brain autonomous agent network. As empirical evidence of function, the system generated six provisional patent applications across four independent scientific domains — post-quantum cryptography, antimicrobial resistance prediction, PII tokenisation, and AI architecture — within a single calendar month. We formalise the methodology, characterise its failure modes, and argue that adversarial ensemble reasoning with formal verification constitutes a new standard for trustworthy AI-assisted scientific discovery.