{"id":"b6765ed6-0b82-431e-81d7-324a851f8efa","arxiv_id":"2509.24531","paper_version":2,"verdict":"REJECT","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":4,"one_line_summary":"A theoretical and empirical comparison claiming diffusion bridges have lower stochastic-optimal-control cost and greater robustness than flow matching when training data are scarce.","lead":"This paper compares Diffusion Bridge and Flow Matching for distribution-to-distribution transformation, arguing from stochastic optimal control that bridges have lower control cost and from optimal transport that flow matching's linear interpolation degrades with small training sets. It also introduces a latent Transformer backbone for diffusion bridges and benchmarks both methods on image restoration and translation.","discovery_kind":"extension","skeptic_critique":null,"referee_report":null,"author_rebuttal":null,"desk_editor":null,"rs_alignment":null,"lean_confirmation":null,"pith_extraction":null,"created_at":"2026-08-04T13:52:01.826478+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":null,"supporting_citations":[],"review_version":1}