Two things evolve smoothly in time: the signatures themselves (φ), and each person's loading on them (λ) — anchored in germline genetics, so the model builds from and reveals new biology, rather than black box artifacts. Then Bayesian updating does the work: every new diagnosis refines the estimate, turning past into present into a prediction of the future. Every parameter is interpretable and described in our generative framework, scaled to work with biobank level data.