Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels

• URL: http://arxiv.org/abs/2605.12805v1
• Date: Tue, 12 May 2026 22:56:44 GMT
• Title: Discrete MeanFlow: One-Step Generation via Conditional Transition Kernels
• Abstract summary: We introduce Discrete MeanFlow, which replaces the motion of a point with the transport of probability mass over finite states.<n>We prove a MeanFlow identity that relates this finite-interval rate to the instantaneous CTMC generator at the endpoint.<n>We validate the framework on exact finite-state Markov chains, where the learned kernel recovers the analytical ground truth to high precision.
• Score: 16.67543300063058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: MeanFlow enables one-step generation in continuous spaces by learning an average velocity over a time interval rather than the instantaneous velocity field of flow matching. However, discrete state spaces do not have smooth trajectories or spatial derivatives, so the continuous formulation does not directly apply. We introduce Discrete MeanFlow, which replaces the motion of a point with the transport of probability mass over finite states. Our key object is the conditional transition kernel of a continuous-time Markov chain (CTMC), from which we define a mean discrete rate that measures the average change in transition probability over a time interval. We prove a Discrete MeanFlow identity that relates this finite-interval rate to the instantaneous CTMC generator at the endpoint, with the Kolmogorov forward equation replacing the spatial chain rule of continuous MeanFlow. Based on this identity, we parameterize the transition kernel directly using a boundary-by-construction design that guarantees valid probability outputs and exact boundary conditions without auxiliary losses. Since the learned kernel is itself a probability distribution, generation reduces to a single forward pass followed by one categorical draw meaning no iterative denoising, ODE integration, or multi-step refinement is required. We validate the framework on exact finite-state Markov chains, where the learned kernel recovers the analytical ground truth to high precision, and on factorized synthetic sequence generation tasks with varying alphabet sizes and sequence lengths.

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