{"id":"https://openalex.org/W7161812280","doi":"https://doi.org/10.48550/arxiv.2605.19218","title":"Rotation-Aligned Key Channel Pruning for Efficient Vision-Language Model Inference","display_name":"Rotation-Aligned Key Channel Pruning for Efficient Vision-Language Model Inference","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7161812280","doi":"https://doi.org/10.48550/arxiv.2605.19218"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.19218","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19218","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.19218","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028117919","display_name":"Beomseok Kang","orcid":"https://orcid.org/0000-0003-3562-6233"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Beomseok","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136571924","display_name":"Dongwon Jo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jo, Dongwon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125417707","display_name":"Jiwon Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Jiwon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136513993","display_name":"Donghwee Son","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Son, Donghwee","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5003219699","display_name":"Jae\u2010Joon Kim","orcid":"https://orcid.org/0000-0001-5175-8258"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Jae-Joon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"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":0.34950000047683716,"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":0.34950000047683716,"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":0.31630000472068787,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0997999981045723,"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":0.7784000039100647},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.6017000079154968},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5662000179290771},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5554999709129333},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5130000114440918},{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.5013999938964844},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4555000066757202},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.45320001244544983}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7993999719619751},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7784000039100647},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.6017000079154968},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5662000179290771},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5554999709129333},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5130000114440918},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.5013999938964844},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46970000863075256},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4555000066757202},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.45320001244544983},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4438999891281128},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3571000099182129},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35600000619888306},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30480000376701355},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.30399999022483826},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2953000068664551},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2888999879360199},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.25870001316070557}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.19218","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19218","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.19218","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.19218","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":"article"},"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":{"Vision-Language":[0],"Models":[1],"suffer":[2],"severe":[3],"KV":[4,50,172],"cache":[5,51,173],"pressure":[6],"at":[7,61,170],"inference,":[8],"as":[9],"a":[10,42,48,73,98,117,128],"single":[11],"image":[12],"often":[13],"encodes":[14],"into":[15,116],"thousands":[16],"of":[17],"tokens.":[18],"Most":[19],"existing":[20],"methods":[21],"exploit":[22],"token":[23,26],"sparsity":[24],"through":[25],"pruning,":[27],"but":[28,81,90],"permanently":[29],"discarding":[30],"visual":[31,59],"content":[32],"causes":[33],"substantial":[34],"degradation":[35],"on":[36,135,142],"fine-grained":[37],"perception":[38],"tasks.":[39],"This":[40],"motivates":[41],"complementary":[43],"axis,":[44],"feature":[45],"sparsity:":[46],"under":[47,124],"fixed":[49],"budget,":[52],"compressing":[53],"the":[54,62],"channel":[55,68,77,102,114,154],"dimension":[56],"preserves":[57],"more":[58],"tokens":[60],"same":[63],"memory":[64],"cost.":[65],"Prior":[66],"Key":[67,101,153],"pruning":[69,78,103,123,155,165],"methods,":[70],"however,":[71],"face":[72],"structural":[74],"trade-off:":[75],"token-wise":[76],"is":[79,88],"expressive":[80],"unstructured":[82],"and":[83,159],"slow,":[84],"while":[85,162],"head-wise":[86,126],"approach":[87],"hardware-friendly":[89],"less":[91],"robust.":[92],"We":[93],"resolve":[94],"this":[95],"with":[96],"RotateK,":[97],"rotation-based":[99],"structured":[100],"framework.":[104],"RotateK":[105,149],"applies":[106],"an":[107],"online":[108],"PCA-based":[109],"rotation":[110],"that":[111,148],"aligns":[112],"token-dependent":[113],"importance":[115],"shared":[118],"low-dimensional":[119],"subspace,":[120],"enabling":[121],"accurate":[122],"lightweight":[125],"masks;":[127],"fused":[129],"Triton":[130],"attention":[131],"kernel":[132],"operates":[133],"directly":[134],"sparse-channel":[136],"Keys":[137],"for":[138],"efficient":[139],"decoding.":[140],"Experiments":[141],"two":[143],"representative":[144],"VLM":[145],"backbones":[146],"show":[147],"consistently":[150],"outperforms":[151],"prior":[152],"in":[156],"both":[157],"accuracy":[158],"decoding":[160],"latency,":[161],"joint":[163],"token-channel":[164],"improves":[166],"over":[167],"token-only":[168],"baselines":[169],"matched":[171],"budgets.":[174]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-21T00:00:00"}
