Poster
in
Workshop: Exploration in AI Today (EXAIT)
Branched Schrödinger Bridge Matching
Sophia Tang · Yinuo Zhang · Alexander Tong · Pranam Chatterjee, PhD
Keywords: [ distribution matching ] [ stochastic optimal control ] [ Schrödinger bridge ] [ bridge matching ] [ optimal transport ]
Predicting the intermediate trajectories between an initial and target distribution is a central problem in generative modeling. Existing approaches, such as flow matching and Schrödinger Bridge Matching, effectively learn mappings between two distributions by modeling a single stochastic path. However, these methods are inherently limited to unimodal transitions and cannot capture branched or divergent evolution from a common origin to multiple distinct outcomes. To address this, we introduce Branched Schrödinger Bridge Matching (BranchSBM), a novel framework that learns branched Schrödinger bridges. BranchSBM parameterizes multiple time-dependent velocity fields and growth processes, enabling the representation of population-level divergence into multiple terminal distributions. We show that BranchSBM is not only more expressive but also essential for tasks involving multi-path surface navigation, modeling cell fate bifurcations from homogeneous progenitor states, and simulating diverging cellular responses to perturbations.