{"id":"https://openalex.org/W7155525962","doi":"https://doi.org/10.48550/arxiv.2604.21444","title":"HiCrew: Hierarchical Reasoning for Long-Form Video Understanding via Question-Aware Multi-Agent Collaboration","display_name":"HiCrew: Hierarchical Reasoning for Long-Form Video Understanding via Question-Aware Multi-Agent Collaboration","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7155525962","doi":"https://doi.org/10.48550/arxiv.2604.21444"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.21444","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21444","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.2604.21444","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134509347","display_name":"Yuehan Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yuehan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134509919","display_name":"Jingqi Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Jingqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134469867","display_name":"Jiawen Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Jiawen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006837984","display_name":"Xudong Mao","orcid":"https://orcid.org/0000-0002-1952-4176"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mao, Xudong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134551612","display_name":"Baoquan Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Baoquan","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9927999973297119,"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":0.9927999973297119,"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/T10028","display_name":"Topic Modeling","score":0.0008999999845400453,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.000699999975040555,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5437999963760376},{"id":"https://openalex.org/keywords/closed-captioning","display_name":"Closed captioning","score":0.498199999332428},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.4480000138282776},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4142000079154968},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.35089999437332153},{"id":"https://openalex.org/keywords/hierarchical-clustering","display_name":"Hierarchical clustering","score":0.3440000116825104},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.34130001068115234},{"id":"https://openalex.org/keywords/tree-structure","display_name":"Tree structure","score":0.3312000036239624}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8259999752044678},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5437999963760376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5131999850273132},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.498199999332428},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.4480000138282776},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4142000079154968},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.36579999327659607},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C92835128","wikidata":"https://www.wikidata.org/wiki/Q1277447","display_name":"Hierarchical clustering","level":3,"score":0.3440000116825104},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.34130001068115234},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.3312000036239624},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.30230000615119934},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29670000076293945},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.26159998774528503},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.21444","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21444","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.2604.21444","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21444","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":{"Long-form":[0],"video":[1],"understanding":[2],"remains":[3],"fundamentally":[4],"challenged":[5],"by":[6,128],"pervasive":[7],"spatiotemporal":[8],"redundancy":[9],"and":[10,132,143,164],"intricate":[11],"narrative":[12],"dependencies":[13],"that":[14,47,67,82,107,123,168],"span":[15],"extended":[16],"temporal":[17,30,89,163],"horizons.":[18],"While":[19],"recent":[20],"structured":[21],"representations":[22],"compress":[23],"visual":[24,110],"information":[25],"effectively,":[26],"they":[27],"frequently":[28],"sacrifice":[29],"coherence,":[31],"which":[32],"is":[33],"critical":[34],"for":[35],"causal":[36,165],"reasoning.":[37],"Meanwhile,":[38],"existing":[39],"multi-agent":[40,65],"frameworks":[41],"operate":[42],"through":[43,71],"rigid,":[44],"pre-defined":[45],"workflows":[46],"fail":[48],"to":[49,54,87,112],"adapt":[50],"their":[51],"reasoning":[52,166],"strategies":[53],"question-specific":[55],"demands.":[56],"In":[57],"this":[58],"paper,":[59],"we":[60,76,101,118],"introduce":[61],"HiCrew,":[62],"a":[63,78,103,120],"hierarchical":[64,94,172],"framework":[66],"addresses":[68],"these":[69],"limitations":[70],"three":[72],"core":[73],"contributions.":[74],"First,":[75],"propose":[77],"Hybrid":[79],"Tree":[80],"structure":[81],"leverages":[83],"shot":[84],"boundary":[85],"detection":[86],"preserve":[88],"topology":[90],"while":[91],"performing":[92],"relevance-guided":[93],"clustering":[95],"within":[96],"semantically":[97],"coherent":[98],"segments.":[99],"Second,":[100],"develop":[102],"Question-Aware":[104],"Captioning":[105],"mechanism":[106],"synthesizes":[108],"intent-driven":[109],"prompts":[111],"generate":[113],"precision-oriented":[114],"semantic":[115],"descriptions.":[116],"Third,":[117],"integrate":[119],"Planning":[121],"Layer":[122],"dynamically":[124],"orchestrates":[125],"agent":[126],"collaboration":[127],"adaptively":[129],"selecting":[130],"roles":[131],"execution":[133],"paths":[134],"based":[135],"on":[136,141],"question":[137,156],"complexity.":[138],"Extensive":[139],"experiments":[140],"EgoSchema":[142],"NExT-QA":[144],"validate":[145],"the":[146],"effectiveness":[147],"of":[148],"our":[149,171],"approach,":[150],"demonstrating":[151],"strong":[152],"performance":[153],"across":[154],"diverse":[155],"types":[157],"with":[158],"particularly":[159],"pronounced":[160],"gains":[161],"in":[162],"tasks":[167],"benefit":[169],"from":[170],"structure-preserving":[173],"design.":[174]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-25T00:00:00"}