{"id":"https://openalex.org/W7133296884","doi":"https://doi.org/10.48550/arxiv.2603.01236","title":"AgilePruner: An Empirical Study of Attention and Diversity for Adaptive Visual Token Pruning in Large Vision-Language Models","display_name":"AgilePruner: An Empirical Study of Attention and Diversity for Adaptive Visual Token Pruning in Large Vision-Language Models","publication_year":2026,"publication_date":"2026-03-01","ids":{"openalex":"https://openalex.org/W7133296884","doi":"https://doi.org/10.48550/arxiv.2603.01236"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01236","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01236","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.2603.01236","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126427304","display_name":"Changwoo Baek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Baek, Changwoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126373540","display_name":"Jouwon Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Jouwon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127936576","display_name":"Sohyeon Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Sohyeon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5000238164","display_name":"Kyeongbo Kong","orcid":"https://orcid.org/0000-0002-1135-7502"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kong, Kyeongbo","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":0.7666000127792358,"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":0.7666000127792358,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.06419999897480011,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.041099999099969864,"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/pruning","display_name":"Pruning","score":0.8205999732017517},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.7383000254631042},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5468000173568726},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.5200999975204468},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.474700003862381},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.46129998564720154},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.40149998664855957},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.3707999885082245},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.34380000829696655}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8205999732017517},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7802000045776367},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.7383000254631042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6254000067710876},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6134999990463257},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5468000173568726},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.5200999975204468},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.474700003862381},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.46129998564720154},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.40149998664855957},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.3707999885082245},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3675999939441681},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.34380000829696655},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C2781316041","wikidata":"https://www.wikidata.org/wiki/Q1230584","display_name":"Diversity (politics)","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C63882131","wikidata":"https://www.wikidata.org/wiki/Q17122954","display_name":"Strengths and weaknesses","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.30959999561309814},{"id":"https://openalex.org/C59732488","wikidata":"https://www.wikidata.org/wiki/Q2528440","display_name":"Visual analytics","level":3,"score":0.3093999922275543},{"id":"https://openalex.org/C206654554","wikidata":"https://www.wikidata.org/wiki/Q5374247","display_name":"Empirical measure","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.26440000534057617},{"id":"https://openalex.org/C168820333","wikidata":"https://www.wikidata.org/wiki/Q448889","display_name":"Visual inspection","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01236","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01236","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.2603.01236","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01236","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":{"Large":[0],"Vision-Language":[1],"Models":[2],"(LVLMs)":[3],"have":[4],"adopted":[5],"visual":[6,18,68,142],"token":[7,19,69],"pruning":[8,31,95,170,190],"strategies":[9,171],"to":[10,66,120,125],"mitigate":[11],"substantial":[12],"computational":[13],"overhead":[14],"incurred":[15],"by":[16],"extensive":[17],"sequences.":[20],"While":[21],"prior":[22],"works":[23],"primarily":[24],"focus":[25],"on":[26,138,157],"either":[27],"attention-based":[28,126,133],"or":[29],"diversity-based":[30,147],"methods,":[32],"in-depth":[33],"analysis":[34,51,82,90,105],"of":[35,59,78,182],"these":[36,158],"approaches'":[37],"characteristics":[38],"and":[39,62,72,76,195],"limitations":[40],"remains":[41],"largely":[42],"unexplored.":[43],"In":[44],"this":[45],"work,":[46],"we":[47,161],"conduct":[48],"thorough":[49],"empirical":[50,159,184],"using":[52,106],"effective":[53,137],"rank":[54],"(erank)":[55],"as":[56,201,203],"a":[57,179,187],"measure":[58],"feature":[60,100],"diversity":[61,101,113],"attention":[63],"score":[64],"entropy":[65],"investigate":[67],"processing":[70],"mechanisms":[71],"analyze":[73],"the":[74,107,112],"strengths":[75],"weaknesses":[77],"each":[79],"approach.":[80],"Our":[81,87,206],"reveals":[83,110],"two":[84],"insights:":[85],"(1)":[86],"erank-based":[88],"quantitative":[89],"shows":[91],"that":[92,111,132,163],"many":[93],"diversity-oriented":[94],"methods":[96,148],"preserve":[97],"substantially":[98],"less":[99],"than":[102],"intended;":[103],"moreover,":[104],"CHAIR":[108],"dataset":[109],"they":[114],"do":[115],"retain":[116],"is":[117,144],"closely":[118],"tied":[119],"increased":[121],"hallucination":[122],"frequency":[123],"compared":[124],"pruning.":[127],"(2)":[128],"We":[129,176],"further":[130],"observe":[131],"approaches":[134],"are":[135],"more":[136],"simple":[139,188],"images":[140,152],"where":[141],"evidence":[143],"concentrated,":[145],"while":[146],"better":[149],"handle":[150],"complex":[151],"with":[153],"distributed":[154],"features.":[155],"Building":[156],"insights,":[160],"show":[162],"incorporating":[164],"image-aware":[165],"adjustments":[166],"into":[167],"existing":[168],"hybrid":[169],"consistently":[172],"improves":[173],"their":[174],"performance.":[175],"also":[177],"provide":[178],"minimal":[180],"instantiation":[181],"our":[183],"findings":[185],"through":[186],"adaptive":[189],"mechanism,":[191],"which":[192],"achieves":[193],"strong":[194],"reliable":[196],"performance":[197],"across":[198],"standard":[199],"benchmarks":[200],"well":[202],"hallucination-specific":[204],"evaluations.":[205],"project":[207],"page":[208],"available":[209],"at":[210],"https://cvsp-lab.github.io/AgilePruner.":[211]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-04T00:00:00"}