Branched Schrödinger Bridge Matching
Predicting the intermediate trajectories between an initial and target
distribution is a central problem in generative modeling. Existing approaches,
such as flow matching and Schr\"odinger 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 Schr\"odinger Bridge
Matching (BranchSBM), a novel framework that learns branched Schr\"odinger
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.
6849516c42e4f9106973f4d1
Sophia Tang
claimed_verified
2026-01-07T09:29:09.226Z
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Sophia Tang
sophtang
user
sophtang
6849516c42e4f9106973f4d2
Yinuo Zhang
6849516c42e4f9106973f4d3
Alexander Tong
claimed_verified
2026-04-08T16:35:19.870Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/667c6b61c03db9e1086a01ed/tqKw1z5-PCttP5vdvtZ6B.jpeg
Alexander Tong
atong01
user
atong01
6849516c42e4f9106973f4d4
Pranam Chatterjee
claimed_verified
2026-01-08T08:34:54.284Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eo9HiGbvCxHQZ-QJB1Y_o.jpeg
Pranam Chatterjee
pranamanam
user
pranamanam
Branched Schrödinger Bridge Matching
Predicting the intermediate trajectories between an initial and target
distribution is a central problem in generative modeling. Existing approaches,
such as flow matching and Schr\"odinger 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 Schr\"odinger Bridge
Matching (BranchSBM), a novel framework that learns branched Schr\"odinger
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.
BranchSBM, a novel generative modeling framework, extends Schr\"odinger Bridge Matching to model branched stochastic paths and multi-path evolution from a single initial distribution to multiple outcomes.
flow matching Schr\"odinger Bridge Matching Branched Schr\"odinger Bridge Matching BranchSBM time-dependent velocity fields growth processes multi-path surface navigation cell fate bifurcations cellular responses to perturbations