Tracing Generated Samples to Training-Data Clusters in Flow-Matching Models
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, which show that semantic similarity constitutes a strong baseline, while the closed-form trajectory-based attribution is competitive i...