{"id":"https://openalex.org/W7139915495","doi":"https://doi.org/10.48550/arxiv.2603.19217","title":"LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs","display_name":"LVOmniBench: Pioneering Long Audio-Video Understanding Evaluation for Omnimodal LLMs","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7139915495","doi":"https://doi.org/10.48550/arxiv.2603.19217"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19217","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19217","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.2603.19217","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130245499","display_name":"Keda Tao","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tao, Keda","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102272485","display_name":"Yuhua Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Yuhua","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130228025","display_name":"Jia Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130225017","display_name":"Wenjie Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Wenjie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130223539","display_name":"Kele Shao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shao, Kele","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130247197","display_name":"Hesong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Hesong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012173895","display_name":"X. Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xueyi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130239270","display_name":"Xin Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Xin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066857659","display_name":"Junhan Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Junhan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130223931","display_name":"Bohan Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Bohan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130253185","display_name":"Weiqiang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Weiqiang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130243921","display_name":"Jian Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130217085","display_name":"Can Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Can","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130229915","display_name":"Yulun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yulun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130241491","display_name":"Ming-Hsuan Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Ming-Hsuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130244838","display_name":"Huan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Huan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":16,"corresponding_author_ids":["https://openalex.org/A5130245499"],"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9502000212669373,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9502000212669373,"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/T10860","display_name":"Speech and Audio Processing","score":0.005900000222027302,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0044999998062849045,"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/comprehension","display_name":"Comprehension","score":0.6521999835968018},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6184999942779541},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5232999920845032},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.48179998993873596},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45100000500679016},{"id":"https://openalex.org/keywords/duration","display_name":"Duration (music)","score":0.40700000524520874},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.3806999921798706}],"concepts":[{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.6521999835968018},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.635699987411499},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6184999942779541},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5232999920845032},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.48179998993873596},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45100000500679016},{"id":"https://openalex.org/C112758219","wikidata":"https://www.wikidata.org/wiki/Q16038819","display_name":"Duration (music)","level":2,"score":0.40700000524520874},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4025999903678894},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3806999921798706},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3513000011444092},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3434000015258789},{"id":"https://openalex.org/C2778739407","wikidata":"https://www.wikidata.org/wiki/Q165372","display_name":"CLIPS","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2831000089645386},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2567000091075897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19217","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19217","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.2603.19217","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19217","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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6681959629058838}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advancements":[1],"in":[2,99],"omnimodal":[3],"large":[4],"language":[5],"models":[6,148,185],"(OmniLLMs)":[7],"have":[8],"significantly":[9],"improved":[10],"the":[11,39,66,116,155,181],"comprehension":[12,68],"of":[13,41,50,69,118,163,183,187],"audio":[14,25,71],"and":[15,26,72,92,106,129,180],"video":[16,27],"inputs.":[17,146],"However,":[18],"current":[19,137],"evaluations":[20],"primarily":[21],"focus":[22],"on":[23],"short":[24],"clips":[28],"ranging":[29,98],"from":[30,80,101],"10":[31,102],"seconds":[32],"to":[33,37,103,113],"5":[34],"minutes,":[35,105],"failing":[36],"reflect":[38],"demands":[40],"real-world":[42],"applications,":[43],"where":[44],"videos":[45,78],"typically":[46],"run":[47],"for":[48,65],"tens":[49],"minutes.":[51],"To":[52],"address":[53],"this":[54,169],"critical":[55],"gap,":[56],"we":[57],"introduce":[58],"LVOmniBench,":[59],"a":[60,160],"new":[61],"benchmark":[62],"designed":[63],"specifically":[64],"cross-modal":[67,190],"long-form":[70,194],"video.":[73],"This":[74],"dataset":[75],"comprises":[76,95],"high-quality":[77],"sourced":[79],"open":[81],"platforms":[82],"that":[83,136,168],"feature":[84],"rich":[85],"audio-visual":[86,145,195],"dynamics.":[87],"Through":[88],"rigorous":[89],"manual":[90],"selection":[91],"annotation,":[93],"LVOmniBench":[94,111],"275":[96],"videos,":[97],"duration":[100],"90":[104],"1,014":[107],"question-answer":[108],"(QA)":[109],"pairs.":[110],"aims":[112],"rigorously":[114],"evaluate":[115],"capabilities":[117],"OmniLLMs":[119,138],"across":[120],"domains,":[121],"including":[122],"long-term":[123],"memory,":[124],"temporal":[125],"localization,":[126],"fine-grained":[127],"understanding,":[128],"multimodal":[130],"perception.":[131],"Our":[132],"extensive":[133],"evaluation":[134],"reveals":[135],"encounter":[139],"significant":[140],"challenges":[141],"when":[142],"processing":[143],"extended":[144],"Open-source":[147],"generally":[149],"achieve":[150],"accuracies":[151],"below":[152],"35%,":[153],"whereas":[154],"Gemini":[156],"3":[157],"Pro":[158],"reaches":[159],"peak":[161],"accuracy":[162],"approximately":[164],"65%.":[165],"We":[166],"anticipate":[167],"dataset,":[170],"along":[171],"with":[172],"our":[173],"empirical":[174],"findings,":[175],"will":[176],"stimulate":[177],"further":[178],"research":[179],"development":[182],"advanced":[184],"capable":[186],"resolving":[188],"complex":[189],"understanding":[191],"problems":[192],"within":[193],"contexts.":[196]},"counts_by_year":[],"updated_date":"2026-03-21T06:36:02.116451","created_date":"2026-03-21T00:00:00"}