Mechanistic Interpretability Findings That Generalize: Chaperone-Dependent Folding in ESMFold2

Interpretability findings in scientific models are often supported by post hoc plausibility rather than held-out tests. We introduce a workflow that turns interpretability outputs into statistically testable predictions: feature terms discovered in examples sharing a biological property should have stronger effects in unseen members than in controls. We apply this workflow to an SAE over ESMFold2 representations and to GroEL/GroES clients: proteins that require this chaperone system for folding in vivo. Perturbations in 25 discovery clients identify 67 stable SAE terms. Their ablations have larger effects in eight held-out clients than in 19 matched controls for 56 out of 67 terms ($p=2\times 10^{-12}$), and their selected-to-random effect ratio is $\sim 24$% higher in clients than in controls. When multiple selected terms are ablated together, client-control separation increases from approximately 3.8- to 7.3-fold across a set of randomly subsampled terms. After these tests, interpretations of the selected terms connect to known client properties and motivate new hypotheses about client-cavity charge patterning, compensatory core packing, and delayed interface closure. Our contribution is a cohort-conditioned discovery-and-evaluation workflow that turns latent feature interactions into held-out tests of biological selectivity. The framework accommodates multiple discovery methods and uses the selected interventions to nominate biological hypotheses.