Reason Without Reverence: AI as a Partner in Scientific Validation

Abstract Foundational scientific work can sometimes be shielded from logical scrutiny by reputation or inertia. Given this challenge to objectivity, can artificial intelligence serve as an impartial partner for rigorous validation? We tasked eight state-of-the-art generative AI systems, acting as reasoning tools, with diagnosing a specific algebraic inconsistency within a standard Special Relativity derivation. Operating via symbolic reasoning under strict logical constraints, all eight independently identified the root cause – a specific substitution error. Critically, several models also identified how the derivation's apparent validity relies on a restricting special-case condition (x=ct) that mathematically masks the inconsistency. This overall success demonstrates AI capability for deep, constrained logical analysis seemingly beyond simple pattern matching, highlighting the potential for structured human-AI partnerships in scientific validation. It prompts reconsideration of knowledge vetting when AI collaborators can analyze arguments 'without reverence' for their authoritative source.