{"id":"https://openalex.org/W7161677571","doi":"https://doi.org/10.48550/arxiv.2605.17244","title":"Drift Flow Matching","display_name":"Drift Flow Matching","publication_year":2026,"publication_date":"2026-05-17","ids":{"openalex":"https://openalex.org/W7161677571","doi":"https://doi.org/10.48550/arxiv.2605.17244"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17244","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17244","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.17244","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030954063","display_name":"Chenrui Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Chenrui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136464761","display_name":"Xi Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Xi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136493829","display_name":"Lin Zhao","orcid":"https://orcid.org/0000-0001-7207-9246"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136502619","display_name":"Tianyang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Tianyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136491802","display_name":"Ferdinando Fioretto","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fioretto, Ferdinando","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136488382","display_name":"Yanning Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Yanning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.2678000032901764,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.2678000032901764,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.22470000386238098,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.06689999997615814,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6161999702453613},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5687999725341797},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5681999921798706},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.5462999939918518},{"id":"https://openalex.org/keywords/generality","display_name":"Generality","score":0.5159000158309937},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4916999936103821},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4584999978542328}],"concepts":[{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6161999702453613},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6043000221252441},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5687999725341797},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5681999921798706},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.5462999939918518},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.52920001745224},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.5159000158309937},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4916999936103821},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4584999978542328},{"id":"https://openalex.org/C62064638","wikidata":"https://www.wikidata.org/wiki/Q553878","display_name":"Design for manufacturability","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.3398999869823456},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.3368000090122223},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.32760000228881836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2989000082015991},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.2581999897956848}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17244","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17244","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.17244","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17244","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Iterative":[0],"generative":[1,54,97],"models":[2,9],"such":[3,38],"as":[4],"Flow":[5,46,90],"Matching":[6,47,91],"and":[7,88,93,113,118],"Diffusion":[8],"have":[10],"demonstrated":[11],"strong":[12],"test-time":[13],"scaling":[14],"behavior,":[15],"where":[16],"additional":[17],"inference":[18,76],"computation":[19,103],"can":[20,100],"improve":[21],"generation":[22,35,70],"quality.":[23],"In":[24,40],"contrast,":[25],"Drift":[26,45,86],"Models":[27,87],"offer":[28],"efficient":[29],"one-step":[30,85],"generation,":[31],"but":[32],"their":[33],"direct":[34,65],"paradigm":[36,98],"limits":[37],"flexibility.":[39],"this":[41],"work,":[42],"we":[43],"propose":[44],"(DFM),":[48],"a":[49,95],"framework":[50],"that":[51,99],"connects":[52],"drifting":[53],"modeling":[55],"with":[56],"flow-based":[57],"iterative":[58],"generation.":[59],"DFM":[60],"preserves":[61],"the":[62,82,116,121],"efficiency":[63],"of":[64,120],"transport":[66],"maps":[67],"while":[68],"enabling":[69],"to":[71,104],"be":[72],"refined":[73],"through":[74],"multiple":[75],"steps":[77],"when":[78],"desired.":[79],"This":[80],"bridges":[81],"gap":[83],"between":[84],"multi-step":[89],"methods,":[92],"provides":[94],"novel":[96],"adapt":[101],"sampling":[102],"different":[105,111],"quality--efficiency":[106],"requirements.":[107],"Extensive":[108],"experiments":[109],"across":[110],"tasks":[112],"datasets":[114],"demonstrate":[115],"effectiveness":[117],"generality":[119],"proposed":[122],"framework.":[123]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}