WFR-MFM: One-Step Inference for Dynamic Unbalanced Optimal Transport
This addresses a key bottleneck for scalable applications in single-cell biology, such as perturbation response prediction, by making inference much faster.
The paper tackles the challenge of slow inference in dynamic unbalanced optimal transport for single-cell biology by proposing a mean-flow framework that enables one-step generation without trajectory simulation. It achieves orders-of-magnitude faster inference while maintaining high predictive accuracy on synthetic and real datasets.
Reconstructing dynamical evolution from limited observations is a fundamental challenge in single-cell biology, where dynamic unbalanced optimal transport provides a principled framework for modeling coupled transport and mass variation. However, existing approaches rely on trajectory simulation at inference time, making inference a key bottleneck for scalable applications. In this work, we propose a mean-flow framework for unbalanced flow matching that summarizes both transport and mass-growth dynamics over arbitrary time intervals using mean velocity and mass-growth fields, enabling fast one-step generation without trajectory simulation. To solve dynamic unbalanced optimal transport under the Wasserstein-Fisher-Rao geometry, we further build on this framework to develop Wasserstein-Fisher-Rao Mean Flow Matching (WFR-MFM). Across synthetic and real single-cell RNA sequencing datasets, WFR-MFM achieves orders-of-magnitude faster inference than a range of existing baselines while maintaining high predictive accuracy, and enables efficient perturbation response prediction on large synthetic datasets with thousands of conditions.