{"id":"https://openalex.org/W7160304787","doi":"https://doi.org/10.48550/arxiv.2605.02829","title":"Compress Then Adapt? No, Do It Together via Task-aware Union of Subspaces","display_name":"Compress Then Adapt? No, Do It Together via Task-aware Union of Subspaces","publication_year":2026,"publication_date":"2026-05-04","ids":{"openalex":"https://openalex.org/W7160304787","doi":"https://doi.org/10.48550/arxiv.2605.02829"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.02829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02829","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.2605.02829","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135308969","display_name":"Jingze Ge","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ge, Jingze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135360022","display_name":"Yun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135399891","display_name":"Xue Geng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geng, Xue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135311401","display_name":"Wanqi Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Wanqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135403845","display_name":"Wang Zhe Mark","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mark, Wang Zhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135358024","display_name":"Min Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135343750","display_name":"Xulei Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Xulei","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":0.33799999952316284,"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":0.33799999952316284,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.1860000044107437,"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":0.12370000034570694,"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/compression","display_name":"Compression (physics)","score":0.6703000068664551},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.6399000287055969},{"id":"https://openalex.org/keywords/linear-subspace","display_name":"Linear subspace","score":0.5860000252723694},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.510699987411499},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.4115000069141388},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.40529999136924744},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.38609999418258667},{"id":"https://openalex.org/keywords/coupling","display_name":"Coupling (piping)","score":0.33469998836517334},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.32690000534057617}],"concepts":[{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.6703000068664551},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6492999792098999},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.6399000287055969},{"id":"https://openalex.org/C12362212","wikidata":"https://www.wikidata.org/wiki/Q728435","display_name":"Linear subspace","level":2,"score":0.5860000252723694},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.510699987411499},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5073999762535095},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.4115000069141388},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.40529999136924744},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.38609999418258667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3476000130176544},{"id":"https://openalex.org/C131584629","wikidata":"https://www.wikidata.org/wiki/Q4308705","display_name":"Coupling (piping)","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C25797200","wikidata":"https://www.wikidata.org/wiki/Q828137","display_name":"Compression ratio","level":3,"score":0.30000001192092896},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2867000102996826},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2851000130176544},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C65682993","wikidata":"https://www.wikidata.org/wiki/Q1056451","display_name":"Reflection (computer programming)","level":2,"score":0.266400009393692},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25940001010894775},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.25760000944137573},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.02829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02829","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.2605.02829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.02829","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":0.44176721572875977,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Adapting":[0],"large":[1],"pretrained":[2,93],"models":[3],"to":[4],"diverse":[5],"tasks":[6],"is":[7],"now":[8],"routine,":[9],"yet":[10],"the":[11,38,92,122,126,134,198,215],"two":[12],"dominant":[13],"strategies":[14],"of":[15,65],"parameter-efficient":[16],"fine-tuning":[17],"(PEFT)":[18],"and":[19,32,44,59,73,83,112,129,158,209],"low-rank":[20,99,147],"compression":[21,72,138],"are":[22],"typically":[23],"composed":[24],"in":[25],"sequence.":[26],"This":[27,119],"decoupled":[28],"practice":[29],"first":[30],"compresses":[31],"then":[33],"fine-tunes":[34],"adapters,":[35],"potentially":[36],"misaligning":[37],"compressed":[39,127],"subspace":[40,128],"with":[41,61,91,139],"downstream":[42,130],"objectives":[43,131],"squandering":[45],"a":[46,62,67,76,97,115,145],"global":[47],"parameter":[48],"budget.":[49,218],"To":[50],"overcome":[51],"this":[52,102],"limitation,":[53],"we":[54],"introduce":[55],"JACTUS":[56,80,163,188],"(Joint":[57],"Adaptation":[58],"Compression":[60],"Task-aware":[63],"Union":[64],"Subspaces),":[66],"single":[68],"framework":[69],"that":[70,149],"unifies":[71],"adaptation.":[74],"From":[75],"small":[77],"calibration":[78],"set,":[79],"estimates":[81],"input":[82],"pre-activation":[84],"gradient":[85],"covariances,":[86],"forms":[87],"their":[88],"orthogonal":[89],"union":[90],"weight":[94],"subspace,":[95],"performs":[96],"projected":[98],"approximation":[100],"inside":[101],"union,":[103],"allocates":[104],"rank":[105],"globally":[106],"by":[107,132],"marginal":[108],"gain":[109],"per":[110],"parameter,":[111],"trains":[113],"only":[114],"compact":[116],"core":[117],"matrix.":[118],"explicitly":[120],"mitigates":[121],"potential":[123],"misalignment":[124],"between":[125],"coupling":[133],"directions":[135],"preserved":[136],"for":[137,142],"those":[140],"required":[141],"adaptation,":[143],"yielding":[144],"deployable":[146],"model":[148],"avoids":[150],"retaining":[151],"full":[152],"frozen":[153],"weights":[154],"while":[155],"enabling":[156],"fast":[157],"robust":[159],"tuning.":[160],"On":[161,186],"vision,":[162],"attains":[164],"an":[165,190],"average":[166,192],"89.2%":[167],"accuracy":[168],"on":[169,193],"ViT-Base":[170],"across":[171],"eight":[172],"datasets":[173],"at":[174,197],"80%":[175,200],"retained":[176],"parameters,":[177],"surpassing":[178],"strong":[179],"100%":[180,204],"PEFT":[181,205],"baselines":[182],"(e.g.,":[183,206],"DoRA":[184,207],"87.9%).":[185],"language,":[187],"achieves":[189],"80.9%":[191],"Llama2-7B":[194],"commonsense":[195],"QA":[196],"same":[199,216],"retained-parameter":[201],"budget,":[202],"outperforming":[203],"79.7%)":[208],"exceeding":[210],"prior":[211],"compress-then-finetune":[212],"pipelines":[213],"under":[214],"ratained-parameter":[217],"We":[219],"will":[220],"release":[221],"code.":[222]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-06T00:00:00"}