We propose explicit and interpretable one-step generation framework that retains the advantages of traditional diffusion models, such as access to intermediate states and fine-grained control, while enabling fast sampling.

We propose FlowMixer, a single-layer architecture using semi-group properties to eliminate neural depth search, achieving competitive multivariate forecasting with interpretable Kronecker-Koopman eigenmodes and algebraic horizon manipulation.