{"id":"https://openalex.org/W7161975950","doi":"https://doi.org/10.48550/arxiv.2605.20600","title":"Head-Aware Key-Value Compression for Efficient Autoregressive Image Generation","display_name":"Head-Aware Key-Value Compression for Efficient Autoregressive Image Generation","publication_year":2026,"publication_date":"2026-05-20","ids":{"openalex":"https://openalex.org/W7161975950","doi":"https://doi.org/10.48550/arxiv.2605.20600"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.20600","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20600","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.2605.20600","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5113395536","display_name":"Guotao Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Guotao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136721347","display_name":"Baoquan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Baoquan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136708849","display_name":"Zhiyuan Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Zhiyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136632588","display_name":"Yunming Ye","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ye, Yunming","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.7275999784469604,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.7275999784469604,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.11959999799728394,"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/T11019","display_name":"Image Enhancement Techniques","score":0.05420000106096268,"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/autoregressive-model","display_name":"Autoregressive model","score":0.8198999762535095},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.6714000105857849},{"id":"https://openalex.org/keywords/cache","display_name":"Cache","score":0.6638000011444092},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.45570001006126404},{"id":"https://openalex.org/keywords/cpu-cache","display_name":"CPU cache","score":0.42239999771118164},{"id":"https://openalex.org/keywords/image-compression","display_name":"Image compression","score":0.4059000015258789},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3797999918460846}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.8198999762535095},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7978000044822693},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.6714000105857849},{"id":"https://openalex.org/C115537543","wikidata":"https://www.wikidata.org/wiki/Q165596","display_name":"Cache","level":2,"score":0.6638000011444092},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.45570001006126404},{"id":"https://openalex.org/C189783530","wikidata":"https://www.wikidata.org/wiki/Q352090","display_name":"CPU cache","level":3,"score":0.42239999771118164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4203000068664551},{"id":"https://openalex.org/C13481523","wikidata":"https://www.wikidata.org/wiki/Q412438","display_name":"Image compression","level":4,"score":0.4059000015258789},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3797999918460846},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3750999867916107},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3617999851703644},{"id":"https://openalex.org/C38556500","wikidata":"https://www.wikidata.org/wiki/Q13404475","display_name":"Cache algorithms","level":4,"score":0.323199987411499},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.322299987077713},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C194657046","wikidata":"https://www.wikidata.org/wiki/Q7394685","display_name":"STAR model","level":4,"score":0.3133000135421753},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.31279999017715454},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3084999918937683},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2619999945163727}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.20600","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20600","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.2605.20600","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20600","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Autoregressive":[0],"(AR)":[1],"visual":[2,23],"generation":[3,242],"has":[4,27],"achieved":[5],"remarkable":[6],"performance":[7],"but":[8],"suffers":[9],"from":[10],"high":[11],"memory":[12,45,70],"usage":[13,46],"and":[14,47,127,195,214],"low":[15],"throughput,":[16],"as":[17],"it":[18,206],"requires":[19,207],"caching":[20],"previously":[21],"generated":[22],"tokens.":[24,181],"Recent":[25],"research":[26],"shown":[28],"that":[29,77,178,185,205],"retaining":[30],"only":[31],"a":[32,54,106,169,223],"few":[33],"lines":[34],"of":[35,143],"cache":[36,111,149],"tokens":[37,174],"can":[38,188],"maintain":[39],"high-quality":[40],"images":[41],"while":[42,94],"significantly":[43],"reducing":[44],"improving":[48],"throughput.":[49],"However,":[50],"these":[51],"methods":[52],"allocate":[53],"fixed":[55],"budget":[56],"to":[57,68,124,130,147,228],"each":[58,144,159],"attention":[59,65,78,85,145,163],"head,":[60],"overlooking":[61],"the":[62,141,156,192],"heterogeneity":[63],"among":[64],"heads,":[66],"leading":[67],"suboptimal":[69],"allocation.":[71],"In":[72],"this":[73,102],"paper,":[74],"we":[75,104,221],"observe":[76,153],"heads":[79,89,126,131],"across":[80,165,217,238],"different":[81,218],"layers":[82],"exhibit":[83],"diverse":[84],"patterns,":[86],"where":[87],"some":[88],"focus":[90],"on":[91,101],"local":[92],"neighborhoods":[93],"others":[95],"capture":[96],"broader":[97,133],"contextual":[98],"dependencies.":[99],"Based":[100],"insight,":[103],"propose":[105],"novel":[107],"head-aware":[108],"key-value":[109],"(KV)":[110],"compression":[112,199],"framework":[113],"for":[114,172,179,197],"autoregressive":[115,240],"image":[116,241],"generation,":[117],"called":[118],"HeadKV,":[119],"which":[120],"assigns":[121],"smaller":[122],"budgets":[123,129],"locality-biased":[125],"larger":[128],"with":[132,177],"attention.":[134],"A":[135],"key":[136],"challenge":[137],"lies":[138],"in":[139],"identifying":[140],"type":[142],"head":[146,160,186],"guide":[148],"compression.":[150],"We":[151],"further":[152],"that,":[154],"within":[155],"same":[157],"layer,":[158],"exhibits":[161],"consistent":[162,176],"patterns":[164],"token":[166],"positions,":[167],"\\emph{i.e.},":[168],"head's":[170],"behavior":[171],"early":[173,193],"remains":[175],"later":[180],"This":[182],"insight":[183],"suggests":[184],"types":[187],"be":[189],"identified":[190],"during":[191],"stage":[194],"reused":[196],"KV":[198],"throughout":[200],"generation.":[201],"Its":[202],"advantage":[203],"is":[204],"no":[208],"additional":[209],"training":[210],"or":[211],"dataset-level":[212],"statistics":[213],"generalizes":[215],"seamlessly":[216],"inputs.":[219],"Moreover,":[220],"design":[222],"Stratified":[224],"Token":[225],"Eviction":[226],"strategy":[227],"effectively":[229],"preserve":[230],"long-range":[231],"information.":[232],"Extensive":[233],"experiments":[234],"demonstrate":[235],"its":[236],"effectiveness":[237],"multiple":[239],"models.":[243]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-22T00:00:00"}
