{"id":"https://openalex.org/W7127391320","doi":"https://doi.org/10.48550/arxiv.2602.01329","title":"FlowCast: Trajectory Forecasting for Scalable Zero-Cost Speculative Flow Matching","display_name":"FlowCast: Trajectory Forecasting for Scalable Zero-Cost Speculative Flow Matching","publication_year":2026,"publication_date":"2026-02-01","ids":{"openalex":"https://openalex.org/W7127391320","doi":"https://doi.org/10.48550/arxiv.2602.01329"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.01329","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.01329","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.01329","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060101081","display_name":"Divya Jyoti Bajpai","orcid":"https://orcid.org/0009-0005-8696-6950"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Bajpai, Divya Jyoti","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100613382","display_name":"Shubham Agarwal","orcid":"https://orcid.org/0000-0003-3290-6328"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agarwal, Shubham","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072649031","display_name":"Apoorv Saxena","orcid":"https://orcid.org/0000-0003-3209-2838"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saxena, Apoorv","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077902428","display_name":"Kuldeep Kulkarni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kulkarni, Kuldeep","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065358142","display_name":"S. Mitra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mitra, Subrata","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124922262","display_name":"Manjesh Kumar Hanawal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hanawal, Manjesh Kumar","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5060101081"],"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8256000280380249,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.8256000280380249,"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"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.0868000015616417,"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"}},{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.01080000028014183,"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/scalability","display_name":"Scalability","score":0.7091000080108643},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.605400025844574},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5882999897003174},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5845999717712402},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5235000252723694},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.49639999866485596},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.47940000891685486},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.474700003862381},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.3864000141620636}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7960000038146973},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.7091000080108643},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.605400025844574},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5882999897003174},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5845999717712402},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5235000252723694},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.49639999866485596},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.47940000891685486},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.474700003862381},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44620001316070557},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3864000141620636},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3425000011920929},{"id":"https://openalex.org/C39920170","wikidata":"https://www.wikidata.org/wiki/Q693083","display_name":"Soundness","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.320499986410141},{"id":"https://openalex.org/C147297375","wikidata":"https://www.wikidata.org/wiki/Q6674930","display_name":"Look-ahead","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C2777027219","wikidata":"https://www.wikidata.org/wiki/Q1284190","display_name":"Constant (computer programming)","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30329999327659607},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2994999885559082},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.2985999882221222},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2818000018596649},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2773999869823456},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2703999876976013},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2606000006198883},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.25920000672340393}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.01329","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.01329","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.01329","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.01329","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":"article"},"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":{"Flow":[0],"Matching":[1],"(FM)":[2],"has":[3],"recently":[4],"emerged":[5],"as":[6,178],"a":[7,21,57,98,123,141],"powerful":[8],"approach":[9],"for":[10],"high-quality":[11],"visual":[12],"generation.":[13,183],"However,":[14],"their":[15,28],"prohibitively":[16],"slow":[17],"inference":[18,64],"due":[19],"to":[20,74,111,180],"large":[22],"number":[23],"of":[24],"denoising":[25],"steps":[26,107],"limits":[27],"potential":[29],"use":[30],"in":[31,108,118,163],"real-time":[32],"or":[33,42,51],"interactive":[34],"applications.":[35],"Existing":[36],"acceleration":[37],"methods,":[38],"like":[39],"distillation,":[40],"truncation,":[41],"consistency":[43],"training,":[44],"either":[45],"degrade":[46],"quality,":[47],"incur":[48],"costly":[49],"retraining,":[50],"lack":[52],"generalization.":[53],"We":[54,138],"propose":[55],"FlowCast,":[56],"training-free":[58],"speculative":[59,150],"generation":[60],"framework":[61,125],"that":[62,69,126,158],"accelerates":[63],"by":[65,82],"exploiting":[66],"the":[67,146],"fact":[68],"FM":[70,131,153],"models":[71],"are":[72],"trained":[73],"preserve":[75],"constant":[76],"velocity.":[77],"FlowCast":[78,121,159],"speculates":[79],"future":[80],"velocity":[81,85],"extrapolating":[83],"current":[84],"without":[86],"incurring":[87],"additional":[88],"time":[89],"cost,":[90],"and":[91,133,144,151,168],"accepts":[92],"it":[93,95],"if":[94],"is":[96,122],"within":[97],"mean-squared":[99],"error":[100],"threshold.":[101],"This":[102],"constant-velocity":[103],"forecasting":[104],"allows":[105],"redundant":[106],"stable":[109],"regions":[110],"be":[112],"aggressively":[113],"skipped":[114],"while":[115],"retaining":[116],"precision":[117],"complex":[119],"ones.":[120],"plug-and-play":[124],"integrates":[127],"seamlessly":[128],"with":[129,174],"any":[130],"model":[132],"requires":[134],"no":[135,175],"auxiliary":[136],"networks.":[137],"also":[139],"present":[140],"theoretical":[142],"analysis":[143],"bound":[145],"worst-case":[147],"deviation":[148],"between":[149],"full":[152,182],"trajectories.":[154],"Empirical":[155],"evaluations":[156],"demonstrate":[157],"achieves":[160],"$>2.5\\times$":[161],"speedup":[162],"image":[164],"generation,":[165,167],"video":[166],"editing":[169],"tasks,":[170],"outperforming":[171],"existing":[172],"baselines":[173],"quality":[176],"loss":[177],"compared":[179],"standard":[181]},"counts_by_year":[],"updated_date":"2026-02-04T23:14:21.375766","created_date":"2026-02-04T00:00:00"}