Tracing Generated Samples to Training-Data Clusters in Flow-Matching Models

Understanding which training samples influence a generated image is an important problem in generative modeling. In flow matching, training samples influence the generated image through the velocity field along the generation trajectory. Removing samples to examine their counterfactual influence changes the velocity field, and the resulting effect on the final image depends on how the change propagates through the trajectory. Consequently, local changes in the velocity field do not necessarily predict the final counterfactual effect. This work investigates attribution in flow-matching models through a hybrid analytical--learned approach, and uses it to derive trajectory-based attribution scores at the cluster level. Our results show that attribution in flow matching depends not only on training-sample similarity, but also on the latent representation, counterfactual metric, trajectory dynamics, and how influence is propagated to the final output.