{"id":"https://openalex.org/W7148410341","doi":"https://doi.org/10.48550/arxiv.2604.00677","title":"CL-VISTA: Benchmarking Continual Learning in Video Large Language Models","display_name":"CL-VISTA: Benchmarking Continual Learning in Video Large Language Models","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148410341","doi":"https://doi.org/10.48550/arxiv.2604.00677"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00677","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00677","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.2604.00677","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132787816","display_name":"Haiyang Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Haiyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102159687","display_name":"Yichen Shi","orcid":"https://orcid.org/0009-0001-9749-7855"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Yichen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132789139","display_name":"Fei Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Fei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132802775","display_name":"Wenzhuo Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Wenzhuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132826888","display_name":"Hongbo Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Hongbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062508341","display_name":"Fanhu Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Fanhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132820828","display_name":"Shijie Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Shijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132802847","display_name":"Da-Han Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Da-Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132788351","display_name":"Xu-Yao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xu-Yao","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6039999723434448,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.6039999723434448,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.31130000948905945,"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":0.02879999950528145,"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/benchmarking","display_name":"Benchmarking","score":0.8246999979019165},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.7106000185012817},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.5242000222206116},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.510699987411499},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.4172999858856201},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.40459999442100525},{"id":"https://openalex.org/keywords/compromise","display_name":"Compromise","score":0.39149999618530273},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.3869999945163727}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8246999979019165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7886999845504761},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.7106000185012817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5752000212669373},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5310999751091003},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.5242000222206116},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.510699987411499},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4172999858856201},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.40459999442100525},{"id":"https://openalex.org/C46355384","wikidata":"https://www.wikidata.org/wiki/Q726686","display_name":"Compromise","level":2,"score":0.39149999618530273},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.3869999945163727},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3582000136375427},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.30709999799728394},{"id":"https://openalex.org/C61272859","wikidata":"https://www.wikidata.org/wiki/Q7834031","display_name":"Transferability","level":3,"score":0.29429998993873596},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.2881999909877777},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25600001215934753},{"id":"https://openalex.org/C2777617010","wikidata":"https://www.wikidata.org/wiki/Q18957","display_name":"Mainstream","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00677","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00677","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.2604.00677","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00677","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":[{"score":0.42884355783462524,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Video":[0],"Large":[1],"Language":[2],"Models":[3],"(Video-LLMs)":[4],"require":[5],"continual":[6,64,183],"learning":[7,184],"to":[8,10,124,165],"adapt":[9],"non-stationary":[11],"real-world":[12],"data.":[13],"However,":[14],"existing":[15],"benchmarks":[16,34],"fall":[17],"short":[18],"of":[19,67,140],"evaluating":[20],"modern":[21],"foundation":[22,187],"models:":[23],"many":[24],"still":[25],"rely":[26],"on":[27,50],"models":[28],"without":[29],"large-scale":[30],"pre-training,":[31],"and":[32,47,77,111,172],"prevailing":[33],"typically":[35],"partition":[36],"a":[37,60,96,119,146],"single":[38,150],"dataset":[39],"into":[40],"sub-tasks,":[41],"resulting":[42],"in":[43,185],"high":[44],"task":[45],"redundancy":[46],"negligible":[48],"forgetting":[49,163],"pre-trained":[51],"Video-LLMs.":[52,68],"To":[53,89],"address":[54],"these":[55],"limitations,":[56],"we":[57,94],"propose":[58],"CL-VISTA,":[59],"benchmark":[61],"tailored":[62],"for":[63,181],"video":[65,121],"understanding":[66,122],"By":[69],"curating":[70],"8":[71],"diverse":[72],"tasks":[73],"spanning":[74],"perception,":[75],"understanding,":[76],"reasoning,":[78],"CL-VISTA":[79,177],"induces":[80],"substantial":[81],"distribution":[82],"shifts":[83],"that":[84,159],"effectively":[85],"expose":[86],"catastrophic":[87,162],"forgetting.":[88],"systematically":[90],"assess":[91,125],"CL":[92,127,143],"methods,":[93],"establish":[95],"comprehensive":[97],"evaluation":[98],"framework":[99],"comprising":[100],"6":[101],"distinct":[102],"protocols":[103],"across":[104,155],"3":[105],"critical":[106,179],"dimensions:":[107],"performance,":[108],"computational":[109,171],"efficiency,":[110],"memory":[112,173],"footprint.":[113],"Notably,":[114],"the":[115],"performance":[116],"dimension":[117],"incorporates":[118],"general":[120],"assessment":[123],"whether":[126],"methods":[128,144],"genuinely":[129],"enhance":[130],"foundational":[131],"intelligence":[132],"or":[133,168],"merely":[134],"induce":[135],"task-specific":[136],"overfitting.":[137],"Extensive":[138],"benchmarking":[139],"10":[141],"mainstream":[142],"reveals":[145],"fundamental":[147],"trade-off:":[148],"no":[149],"approach":[151],"achieves":[152],"universal":[153],"superiority":[154],"all":[156],"dimensions.":[157],"Methods":[158],"successfully":[160],"mitigate":[161],"tend":[164],"compromise":[166],"generalization":[167],"incur":[169],"prohibitive":[170],"overheads.":[174],"We":[175],"hope":[176],"provides":[178],"insights":[180],"advancing":[182],"multimodal":[186],"models.":[188]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-03T00:00:00"}
