{"id":"https://openalex.org/W7155559485","doi":"https://doi.org/10.48550/arxiv.2604.21221","title":"Sparse Forcing: Native Trainable Sparse Attention for Real-time Autoregressive Diffusion Video Generation","display_name":"Sparse Forcing: Native Trainable Sparse Attention for Real-time Autoregressive Diffusion Video Generation","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7155559485","doi":"https://doi.org/10.48550/arxiv.2604.21221"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.21221","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21221","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.2604.21221","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134555519","display_name":"Boxun Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Boxun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031074685","display_name":"Yuming Du","orcid":"https://orcid.org/0009-0001-2132-8027"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Yuming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091829195","display_name":"Zichang Liu","orcid":"https://orcid.org/0000-0002-5556-8117"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zichang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134516245","display_name":"Siyu Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Siyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134552803","display_name":"Ziyang Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Ziyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134524809","display_name":"Siqi Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Siqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134561202","display_name":"Rajasi Saha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saha, Rajasi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134543363","display_name":"Albert Pumarola","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pumarola, Albert","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134547916","display_name":"Wenchen Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Wenchen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134519188","display_name":"Peng Li","orcid":"https://orcid.org/0009-0002-3355-2269"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Peng","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.7125999927520752,"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":0.7125999927520752,"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":0.044599998742341995,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.0348999984562397,"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/autoregressive-model","display_name":"Autoregressive model","score":0.6656000018119812},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6209999918937683},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.6208999752998352},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.612500011920929},{"id":"https://openalex.org/keywords/forcing","display_name":"Forcing (mathematics)","score":0.5892000198364258},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5435000061988831},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5142999887466431},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.4652000069618225}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7979999780654907},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6656000018119812},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6209999918937683},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.6208999752998352},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.612500011920929},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.5892000198364258},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5435000061988831},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5142999887466431},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4975999891757965},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.4652000069618225},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45260000228881836},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C144745244","wikidata":"https://www.wikidata.org/wiki/Q4927286","display_name":"Blocking (statistics)","level":2,"score":0.4027000069618225},{"id":"https://openalex.org/C56372850","wikidata":"https://www.wikidata.org/wiki/Q1050404","display_name":"Sparse matrix","level":3,"score":0.3953000009059906},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.35850000381469727},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3212999999523163},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.31060001254081726},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.298799991607666},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.2892000079154968},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2703000009059906},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26759999990463257},{"id":"https://openalex.org/C3017489831","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Running time","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.21221","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21221","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.2604.21221","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21221","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":{"We":[0],"introduce":[1],"Sparse":[2,21,133],"Forcing,":[3],"a":[4,36,54,68,91,149],"training-and-inference":[5],"paradigm":[6],"for":[7,103,126],"autoregressive":[8,30],"video":[9],"diffusion":[10,31],"models":[11],"that":[12,73,119,132],"improves":[13,135],"long-horizon":[14],"generation":[15,146],"quality":[16,170],"while":[17,83,147],"reducing":[18],"decoding":[19,151],"latency.":[20],"Forcing":[22,134],"is":[23],"motivated":[24],"by":[25,139],"an":[26,44,115],"empirical":[27],"observation":[28],"in":[29,48],"rollouts:":[32],"attention":[33,122],"concentrates":[34],"on":[35,63,143,164,182],"persistent":[37,81],"subset":[38],"of":[39],"salient":[40],"visual":[41,169],"blocks,":[42],"forming":[43],"implicit":[45],"spatiotemporal":[46],"memory":[47,124],"the":[49,98,136],"KV":[50],"cache,":[51],"and":[52,78,106,123,153,173,177,179,184],"exhibits":[53],"locally":[55],"structured":[56],"block-sparse":[57],"pattern":[58],"within":[59,86],"sliding":[60],"windows.":[61],"Building":[62],"this":[64],"observation,":[65],"we":[66,108],"propose":[67,110],"trainable":[69],"native":[70],"sparsity":[71],"mechanism":[72],"learns":[74],"to":[75,90],"compress,":[76],"preserve,":[77],"update":[79],"these":[80],"blocks":[82],"restricting":[84],"computation":[85],"each":[87],"local":[88,94],"window":[89],"dynamically":[92],"selected":[93],"neighborhood.":[95],"To":[96],"make":[97],"approach":[99],"practical":[100],"at":[101],"scale":[102],"both":[104],"training":[105],"inference,":[107],"further":[109],"Persistent":[111],"Block-Sparse":[112],"Attention":[113],"(PBSA),":[114],"efficient":[116],"GPU":[117],"kernel":[118],"accelerates":[120],"sparse":[121],"updates":[125],"low-latency,":[127],"memory-efficient":[128],"decoding.":[129],"Experiments":[130],"show":[131],"VBench":[137,175],"score":[138],"+0.26":[140],"over":[141],"Self-Forcing":[142],"5-second":[144],"text-to-video":[145],"delivering":[148,167],"1.11-1.17x":[150],"speedup":[152],"42%":[154],"lower":[155],"peak":[156],"KV-cache":[157],"footprint.":[158],"The":[159],"gains":[160],"are":[161],"more":[162],"pronounced":[163],"longer-horizon":[165],"rollouts,":[166],"improved":[168],"with":[171],"+0.68":[172],"+2.74":[174],"improvements,":[176],"1.22x":[178],"1.27x":[180],"speedups":[181],"20-second":[183],"1-minute":[185],"generations,":[186],"respectively.":[187]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-25T00:00:00"}