Reason Without Reverence: Rethinking Scientific Authority in the Age of AI

Scientific discovery aspires to objectivity – but reputational hierarchies and institutional inertia often shield foundational theories from scrutiny. This paper explores how a new class of large language models – reasoning agents – can operate outside these constraints, exposing not only a specific structural inconsistency in the transformation equations of Special Relativity, but also deeper limits in the human-centered structure of knowledge validation itself. Eight state-of-the-art generative AI systems were given a formal derivation task: identify and explain an algebraic inconsistency using only the equations explicitly stated. All eight succeeded – diagnosing the contradiction through symbolic reasoning alone, without recall or heuristic. While most traced it to an omitted substitution step, others surfaced the inconsistency by revealing how a special-case constraint concealed the flaw. This is not merely a correction to a canonical derivation; it is evidence that reasoning agents have emerged – entities capable of independent analysis, logical correction, and constraint-based insight. Their success challenges long-held assumptions about who, or what, is allowed to participate in the reasoning process – and signals a shift in how scientific knowledge is constructed.