Formalized scientific methodology enables rigorous AI-conducted research across domains

Abstract

We formalize scientific methodology--the end-to-end process from question formulation to evidence-grounded writing--as a phase-gated research protocol with explicit return paths and persistent constraints, and instantiate it for general-purpose language models as executable protocol specifications. The formalization decomposes methodology into three complementary layers: a procedural workflow, an integrity discipline, and project governance. Encoded as protocol specifications$ and activated across the lifecycle, these constraints externalize planning and verification artifacts and make integrity-relevant interventions auditable. We validate the approach in six end-to-end projects, including a matched controlled study, where the same agent produced two complete papers with and without the protocol. Across domains, the protocol-constrained agent produced evidence-backed, auditable research outputs--including closed-form derivations, quantitative ablations that resolve modeling design choices, and algorithmic refactors that preserve the objective while changing the computational primitive. In population-genomic applications, it also recovered well-studied biological signals as validity checks, including known admixture targets in the 1000 Genomes Project and Neanderthal-introgressed immune loci on chromosome 21 consistent with prior catalogs. In the controlled study, the protocol-free baseline could still produce a complete manuscript, but integrity-relevant risks were easier to introduce and harder to detect when constraints and artifacts were absent.