Evidence-Gated Research: Statistically Controlled Model Adoption in Adaptive Search

An accepted model becomes the reference for later proposals in adaptive search, so an erroneous replacement can alter hypotheses that have not yet been generated. Evidence-Gated Research (EGR) studies model adoption when improvement must hold across a predeclared set of environments. EGR freezes each challenger before decision evidence is examined, constructs anytime-valid environment-specific evidence under predictable sampling, and passes their intersection–union evidence to an online adoption controller. Under a conditional mean bound for each sampled evidence unit, the procedure controls false discovery rate for the declared all-environment adoption target even when earlier adoptions change later challengers and null hypotheses. In a 5,000-trajectory closed-loop benchmark, development-only e-LOND has persistent-target FDR 0.621, whereas no persistent false-adoption path is observed for the audited EGR variants in that finite run. In matched replay over 600 challenger–incumbent pairs, Stagewise EGR and fixed-anytime IUT have identical adoption events by construction; at \(h = 20\), Stagewise uses 56.1% less decision evidence by stopping resolved components and avoiding later stages after failure. EGR-ALLOC further reallocates saved evidence to unresolved environments. Three-environment public-data and controlled neural studies show similar reductions in decision-evidence cost. These results characterize when sequential evidence can support statistically controlled model replacement in an adaptive search.