{"id":"https://openalex.org/W7161257690","doi":"https://doi.org/10.48550/arxiv.2605.13942","title":"EMA: Efficient Model Adaptation for Learning-based Systems","display_name":"EMA: Efficient Model Adaptation for Learning-based Systems","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161257690","doi":"https://doi.org/10.48550/arxiv.2605.13942"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13942","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13942","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.2605.13942","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120963384","display_name":"Daiyang Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Daiyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136214862","display_name":"Xinyu Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136236790","display_name":"Yihan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136236068","display_name":"Yan Liang","orcid":"https://orcid.org/0000-0003-0767-7199"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136213417","display_name":"Yaqi Qiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiao, Yaqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136247309","display_name":"Fan Lai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lai, Fan","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":0.2224999964237213,"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":0.2224999964237213,"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":0.1876000016927719,"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/T12127","display_name":"Software System Performance and Reliability","score":0.08799999952316284,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/adaptation","display_name":"Adaptation (eye)","score":0.6521000266075134},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5967000126838684},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.555400013923645},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5128999948501587},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.39100000262260437},{"id":"https://openalex.org/keywords/system-deployment","display_name":"System deployment","score":0.37770000100135803},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.35589998960494995},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.3398999869823456}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7458999752998352},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.6521000266075134},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5967000126838684},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.555400013923645},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5128999948501587},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.41620001196861267},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C81293917","wikidata":"https://www.wikidata.org/wiki/Q4189534","display_name":"System deployment","level":3,"score":0.37770000100135803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3653999865055084},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.35589998960494995},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C107645828","wikidata":"https://www.wikidata.org/wiki/Q12070446","display_name":"System model","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3237000107765198},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C52970973","wikidata":"https://www.wikidata.org/wiki/Q2497134","display_name":"Adaptive system","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.301800012588501},{"id":"https://openalex.org/C31352089","wikidata":"https://www.wikidata.org/wiki/Q3750474","display_name":"Systems design","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C2780609101","wikidata":"https://www.wikidata.org/wiki/Q17156588","display_name":"Resource management (computing)","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C104122410","wikidata":"https://www.wikidata.org/wiki/Q1416406","display_name":"Network model","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C203274722","wikidata":"https://www.wikidata.org/wiki/Q7001161","display_name":"Network performance","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C2776857766","wikidata":"https://www.wikidata.org/wiki/Q7832987","display_name":"Training system","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13942","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13942","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.2605.13942","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13942","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":{"Machine":[0],"learning":[1],"(ML)":[2],"is":[3],"increasingly":[4],"applied":[5],"to":[6,87,89,140],"optimize":[7],"system":[8,70,83,105,162,197],"performance":[9,198],"in":[10,30,62],"tasks":[11,22],"such":[12],"as":[13],"resource":[14],"management":[15],"and":[16,33,43,50,72,106,158,174],"network":[17,41,200],"simulation.":[18],"Unlike":[19],"traditional":[20],"ML":[21],"(e.g.,":[23,40,189,199],"image":[24],"classification),":[25],"networked":[26],"systems":[27,55,86,182],"often":[28],"operate":[29],"heterogeneous,":[31],"long-running,":[32],"dynamic":[34],"environment":[35,133],"states,":[36,137],"where":[37],"input":[38,128],"conditions":[39],"loads)":[42],"operational":[44,94],"objectives":[45],"can":[46],"shift":[47],"over":[48],"time":[49],"across":[51],"settings.":[52],"Existing":[53],"learning-based":[54,85,181],"offer":[56],"little":[57],"support":[58],"for":[59,156],"adaptation,":[60],"resulting":[61],"costly":[63,149],"model":[64,81,107,119],"training,":[65],"extensive":[66],"data":[67,152,167],"collection,":[68],"degraded":[69],"performance,":[71],"slow":[73],"responsiveness.":[74],"This":[75],"paper":[76],"presents":[77],"EMA,":[78],"the":[79,127,146,170],"first":[80],"adaptation":[82,187],"supporting":[84],"adapt":[88],"evolving":[90],"environments":[91],"with":[92,134],"minimal":[93],"overhead.":[95],"EMA":[96,185],"takes":[97],"a":[98,131],"system-driven,":[99],"data-centric":[100],"approach":[101],"that":[102,125,184],"accommodates":[103],"diverse":[104],"designs":[108],"while":[109,168,195],"addressing":[110],"two":[111],"key":[112],"deployment":[113],"challenges.":[114],"First,":[115],"it":[116,144],"reduces":[117,186],"expensive":[118],"training":[120,159,173,191],"by":[121,193,202],"introducing":[122],"state":[123,129],"transformers":[124],"align":[126],"of":[130,151],"new":[132],"previously":[135],"similar":[136],"allowing":[138],"models":[139],"warm-start":[141],"adaptation.":[142],"Second,":[143],"addresses":[145],"often-overlooked":[147],"yet":[148],"process":[150],"labeling--collecting":[153],"ground":[154],"truth":[155],"exploring":[157],"on":[160,178],"various":[161],"decisions--by":[163],"prioritizing":[164],"labeling":[165,175],"high-utility":[166],"balancing":[169],"tradeoff":[171],"between":[172],"cost.":[176],"Evaluations":[177],"eight":[179],"representative":[180],"show":[183],"costs":[188],"GPU":[190],"time)":[192],"14.9-42.4%":[194],"improving":[196],"throughput)":[201],"6.9-31.3%.":[203]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}