# Single-Cell Multi-omics Integration

FlowAlign turns conditional flow matching from an inference-time generator into a training-time alignment signal: it first learns a source-conditioned velocity field along interpolation paths between paired samples, freezes it, and trains the source-side mapper on the velocity residual that a candidate representation induces. On the paired PBMC and BMMC single-cell multiome benchmarks it maps ATAC representations into a fixed RNA PCA space with lower FOSCTTM than the compared integration methods and architecture-matched paired baselines, while NMI and ARI are higher. Synthetic data and an image–text alignment task report the same kind of improvement.

FlowAlign turns conditional flow matching from an inference-time generator into a training-time alignment signal: it first learns a source-conditioned velocity field along interpolation paths between paired samples, freezes it, and trains the source-side mapper on the velocity residual that a candidate representation induces. On the paired PBMC and BMMC single-cell multiome benchmarks it maps ATAC representations into a fixed RNA PCA space with lower FOSCTTM than the compared integration methods and architecture-matched paired baselines, while NMI and ARI are higher. Synthetic data and an image–text alignment task report the same kind of improvement.