{"id":"https://openalex.org/W7161995090","doi":"https://doi.org/10.48550/arxiv.2605.21072","title":"Q-ARVD: Quantizing Autoregressive Video Diffusion Models","display_name":"Q-ARVD: Quantizing Autoregressive Video Diffusion Models","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7161995090","doi":"https://doi.org/10.48550/arxiv.2605.21072"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.21072","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21072","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.2605.21072","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136619437","display_name":"Siao Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Siao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136613247","display_name":"Xinyin Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Xinyin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136667027","display_name":"Gongfan Fang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Gongfan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136643810","display_name":"Xingyi Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Xingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136704478","display_name":"Xinchao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xinchao","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/T11165","display_name":"Image and Video Quality Assessment","score":0.5728999972343445,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.5728999972343445,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.16169999539852142,"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/T10741","display_name":"Video Coding and Compression Technologies","score":0.04969999939203262,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.7426000237464905},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.7038000226020813},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.6187999844551086},{"id":"https://openalex.org/keywords/spurious-relationship","display_name":"Spurious relationship","score":0.5227000117301941},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.40939998626708984},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4043000042438507}],"concepts":[{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.7426000237464905},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.7038000226020813},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6425999999046326},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.6187999844551086},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5429999828338623},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.5227000117301941},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.40939998626708984},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38659998774528503},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2782000005245209}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.21072","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21072","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.2605.21072","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.21072","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":{"Autoregressive":[0],"video":[1,13,21],"diffusion":[2,71,89],"models":[3],"(ARVDs)":[4],"have":[5],"emerged":[6],"as":[7],"a":[8,36,45,157,174],"promising":[9],"architecture":[10],"for":[11,18,48,53,69,160,208],"streaming":[12],"generation,":[14],"paving":[15],"the":[16,29,167,180,201,222],"way":[17],"real-time":[19],"interactive":[20],"generation":[22,112],"and":[23,128,147,203,212],"world":[24],"modeling.":[25],"Despite":[26],"their":[27],"potential,":[28],"substantial":[30],"inference":[31],"cost":[32],"of":[33,205,224],"ARVDs":[34,54,74],"remains":[35,55],"major":[37],"obstacle":[38],"to":[39,73,76,215],"practical":[40],"deployment,":[41],"making":[42],"model":[43],"quantization":[44,52,66,80,106,117,181],"natural":[46],"direction":[47],"improving":[49],"efficiency.":[50],"However,":[51],"largely":[56],"unexplored.":[57],"Our":[58],"empirical":[59],"analysis":[60],"shows":[61],"that":[62,82],"directly":[63],"applying":[64],"existing":[65],"schemes":[67],"developed":[68],"standard":[70],"transformers":[72],"leads":[75],"suboptimal":[77],"performance,":[78,190],"revealing":[79],"behaviors":[81],"differ":[83],"from":[84,188],"those":[85],"observed":[86],"in":[87,99,132],"bidirectional":[88],"models.":[90],"In":[91],"this":[92],"paper,":[93],"we":[94,154],"identify":[95],"two":[96],"critical":[97],"challenges":[98],"quantizing":[100],"ARVDs:":[101],"(C1)":[102],"Highly":[103],"unbalanced":[104,169],"frame-wise":[105,170],"sensitivity.":[107],"Error":[108],"accumulation":[109],"during":[110],"autoregressive":[111],"can":[113],"induce":[114],"severely":[115],"skewed":[116],"sensitivity":[118],"across":[119,144],"frames,":[120],"following":[121],"an":[122,193,209],"exponential-like":[123],"decay":[124],"pattern.":[125],"(C2)":[126],"Prominent":[127],"heterogeneous":[129,186],"outlier":[130,138,206],"patterns":[131,141],"weights.":[133],"Weight":[134],"distributions":[135],"exhibit":[136],"pronounced":[137],"channels,":[139],"whose":[140],"vary":[142],"substantially":[143],"layer":[145],"types":[146],"block":[148],"depths.":[149],"To":[150,165,184],"address":[151],"these":[152],"issues,":[153],"propose":[155],"Q-ARVD,":[156],"novel":[158],"framework":[159],"accurate":[161],"ARVD":[162],"quantization.":[163],"(S1)":[164],"tackle":[166],"highly":[168],"sensitivity,":[171],"Q-ARVD":[172,191],"incorporates":[173],"final-quality":[175],"aware":[176],"frame-weighting":[177],"mechanism":[178],"into":[179],"objective.":[182],"(S2)":[183],"prevent":[185],"outliers":[187],"degrading":[189],"introduces":[192],"outlier-aware":[194],"adaptive":[195],"dual-scale":[196],"quantization,":[197],"which":[198],"automatically":[199],"detects":[200],"presence":[202],"quantity":[204],"channels":[207],"arbitrary":[210],"layer,":[211],"isolates":[213],"them":[214],"protect":[216],"normal":[217],"channels.":[218],"Extensive":[219],"experiments":[220],"demonstrate":[221],"superiority":[223],"Q-ARVD.":[225]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-22T00:00:00"}