{"id":"https://openalex.org/W7160509462","doi":"https://doi.org/10.48550/arxiv.2605.04461","title":"Stream-T1: Test-Time Scaling for Streaming Video Generation","display_name":"Stream-T1: Test-Time Scaling for Streaming Video Generation","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160509462","doi":"https://doi.org/10.48550/arxiv.2605.04461"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04461","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04461","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":null,"license_id":null,"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.2605.04461","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047579126","display_name":"Y. Tu","orcid":"https://orcid.org/0000-0003-4673-1653"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tu, Yijing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091315837","display_name":"Shaojin Wu","orcid":"https://orcid.org/0000-0002-7899-0863"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Shaojin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135540118","display_name":"Mengqi Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Mengqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135565646","display_name":"Wenchuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Wenchuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135628763","display_name":"Yuxin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yuxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135596586","display_name":"Chunxiao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Chunxiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135571128","display_name":"Zhendong Mao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mao, Zhendong","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":0.6790000200271606,"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":0.6790000200271606,"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":0.04230000078678131,"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/T11439","display_name":"Video Analysis and Summarization","score":0.039400000125169754,"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/context","display_name":"Context (archaeology)","score":0.6065999865531921},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5343999862670898},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.47110000252723694},{"id":"https://openalex.org/keywords/video-denoising","display_name":"Video denoising","score":0.4657999873161316},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.45410001277923584},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.4180000126361847},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.41290000081062317},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4059000015258789},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.36250001192092896}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8274999856948853},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6065999865531921},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5343999862670898},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.47110000252723694},{"id":"https://openalex.org/C30814859","wikidata":"https://www.wikidata.org/wiki/Q4119603","display_name":"Video denoising","level":5,"score":0.4657999873161316},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.45410001277923584},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4341000020503998},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.4180000126361847},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41359999775886536},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.41290000081062317},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4059000015258789},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37380000948905945},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C65483669","wikidata":"https://www.wikidata.org/wiki/Q3536669","display_name":"Video processing","level":2,"score":0.3474999964237213},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C128840427","wikidata":"https://www.wikidata.org/wiki/Q1302174","display_name":"Motion compensation","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.2865000069141388},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C2780624872","wikidata":"https://www.wikidata.org/wiki/Q852453","display_name":"Motion detection","level":3,"score":0.2648000121116638},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.2524999976158142},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.2513999938964844},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.2513999938964844},{"id":"https://openalex.org/C2985909886","wikidata":"https://www.wikidata.org/wiki/Q193147","display_name":"Spatial coherence","level":3,"score":0.2513999938964844},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04461","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04461","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.04461","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04461","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"Test-Time":[1],"Scaling":[2],"(TTS)":[3],"offers":[4],"a":[5,82],"promising":[6],"direction":[7],"to":[8,47,133,148],"enhance":[9],"video":[10,20,49,91,203,212],"generation":[11,21],"without":[12],"the":[13,45,108,113,129,135,177,188,201],"surging":[14],"costs":[15,32],"of":[16,97,112],"training,":[17],"current":[18,136],"test-time":[19],"methods":[22],"based":[23],"on":[24,206],"diffusion":[25],"models":[26],"suffer":[27],"from":[28,180],"exorbitant":[29],"candidate":[30],"exploration":[31],"and":[33,57,127,157,199,209,224],"lack":[34],"temporal":[35,73,125,159,220],"guidance.":[36],"To":[37],"address":[38],"these":[39],"structural":[40],"bottlenecks,":[41],"we":[42,79],"propose":[43],"shifting":[44],"focus":[46],"streaming":[48,90],"generation.":[50,92],"We":[51],"identify":[52],"that":[53,192],"its":[54],"chunk-level":[55],"synthesis":[56],"few":[58],"denoising":[59],"steps":[60],"are":[61],"intrinsically":[62],"suited":[63],"for":[64,89],"TTS,":[65],"significantly":[66,218],"lowering":[67],"computational":[68],"overhead":[69],"while":[70],"enabling":[71],"fine-grained":[72],"control.":[74],"Driven":[75],"by":[76,161,187],"this":[77],"insight,":[78],"introduced":[80],"Stream-T1,":[81],"pioneering":[83],"comprehensive":[84,211],"TTS":[85],"framework":[86],"exclusively":[87],"tailored":[88],"Specifically,":[93],"Stream-T1":[94,214],"is":[95],"composed":[96],"three":[98],"units:":[99],"(1)":[100],"Stream":[101,139],"-Scaled":[102,140],"Noise":[103],"Propagation,":[104],"which":[105,143,174],"actively":[106],"refines":[107],"initial":[109],"latent":[110],"noise":[111],"generating":[114],"chunk":[115,121],"using":[116],"historically":[117],"proven,":[118],"high-quality":[119],"previous":[120],"noise,":[122],"effectively":[123,197],"establishes":[124],"dependency":[126],"utilizing":[128],"historical":[130],"Gaussian":[131],"prior":[132],"guide":[134],"generation;":[137],"(2)":[138],"Reward":[141],"Pruning,":[142],"comprehensively":[144],"evaluates":[145],"generated":[146,194],"candidates":[147],"strike":[149],"an":[150],"optimal":[151],"balance":[152],"between":[153],"local":[154],"spatial":[155],"aesthetics":[156],"global":[158],"coherence":[160],"integrating":[162],"immediate":[163],"short-term":[164],"assessments":[165],"with":[166],"sliding-window-based":[167],"long-term":[168],"evaluations;":[169],"(3)":[170],"Stream-Scaled":[171],"Memory":[172],"Sinking,":[173],"dynamically":[175],"routes":[176],"context":[178],"evicted":[179],"KV-cache":[181],"into":[182],"distinct":[183],"updating":[184],"pathways":[185],"guided":[186],"reward":[189],"feedback,":[190],"ensuring":[191],"previously":[193],"visual":[195,226],"information":[196],"anchors":[198],"guides":[200],"subsequent":[202],"stream.":[204],"Evaluated":[205],"both":[207],"5s":[208],"30s":[210],"benchmarks,":[213],"demonstrates":[215],"profound":[216],"superiority,":[217],"improving":[219],"consistency,":[221],"motion":[222],"smoothness,":[223],"frame-level":[225],"quality.":[227]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-08T00:00:00"}