{"id":"W4414857280","doi":"10.48550/arxiv.2505.24434","title":"Graph Flow Matching: Enhancing Image Generation with Neighbor-Aware Flow Fields","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pointwise; Flow (mathematics); Vector field; Matching (statistics); Graph; Flow velocity; Scalability; Artificial neural network; Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003255577,0.0004600778,0.0004065582,0.000255961,0.000389369,0.0003820761,0.001637219,0.0003470904,0.00004130526],"category_scores_gemma":[0.00006513619,0.0004295374,0.0001646627,0.0004924749,0.00005091166,0.0003170358,0.001345275,0.001436195,0.0001210262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001070878,"about_ca_system_score_gemma":0.0003415566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001239934,"about_ca_topic_score_gemma":0.0003795458,"domain_scores_codex":[0.9973177,0.0001620329,0.0004606801,0.001264283,0.0003808301,0.0004144869],"domain_scores_gemma":[0.9971725,0.0001518308,0.000284858,0.002005653,0.0002502337,0.0001349503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003823687,0.000496796,0.1039232,0.0009291709,0.0005200125,0.000116177,0.008867919,0.764177,0.0130833,0.009657959,0.00303734,0.09515288],"study_design_scores_gemma":[0.0003283448,0.00005057524,0.03795966,0.0002623304,0.00005287221,0.00001177407,0.00002138969,0.9525603,0.004683044,0.003115966,0.0002708927,0.0006828647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2165052,0.00006068311,0.7780141,0.002611391,0.000561867,0.0004448582,0.00002042692,0.0005290667,0.001252479],"genre_scores_gemma":[0.7576426,0.00002665145,0.2405551,0.0005943518,0.0003431761,0.0002410128,0.0001480193,0.00002653713,0.0004226106],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5411374,"threshold_uncertainty_score":0.9998156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02261056781284036,"score_gpt":0.2773195554684678,"score_spread":0.2547089876556274,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}