{"id":"https://openalex.org/W7125531014","doi":"https://doi.org/10.48550/arxiv.2601.15657","title":"Integrating Knowledge Distillation Methods: A Sequential Multi-Stage Framework","display_name":"Integrating Knowledge Distillation Methods: A Sequential Multi-Stage Framework","publication_year":2026,"publication_date":"2026-01-22","ids":{"openalex":"https://openalex.org/W7125531014","doi":"https://doi.org/10.48550/arxiv.2601.15657"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.15657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.15657","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.2601.15657","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080610079","display_name":"Yinxi Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Tian, Yinxi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101001311","display_name":"Changwu Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Changwu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066818082","display_name":"Kui Tang","orcid":"https://orcid.org/0000-0002-9908-4448"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Ke","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123666474","display_name":"Xin Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Xin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5080610079"],"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.6556000113487244,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.6556000113487244,"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/T11122","display_name":"Online Learning and Analytics","score":0.08649999648332596,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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":0.03460000082850456,"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/weighting","display_name":"Weighting","score":0.7961999773979187},{"id":"https://openalex.org/keywords/resource","display_name":"Resource (disambiguation)","score":0.5605000257492065},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.5565000176429749},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5062999725341797},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.43619999289512634},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4196999967098236},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4101000130176544},{"id":"https://openalex.org/keywords/relation","display_name":"Relation (database)","score":0.39809998869895935},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.36910000443458557}],"concepts":[{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.7961999773979187},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7285000085830688},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.5605000257492065},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5062999725341797},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.43619999289512634},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4196999967098236},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4101000130176544},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4049000144004822},{"id":"https://openalex.org/C25343380","wikidata":"https://www.wikidata.org/wiki/Q277521","display_name":"Relation (database)","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.36910000443458557},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3652999997138977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3488999903202057},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.34689998626708984},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C84685590","wikidata":"https://www.wikidata.org/wiki/Q1540472","display_name":"Knowledge engineering","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.15657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.15657","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.2601.15657","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.15657","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":{"Knowledge":[0,69],"distillation":[1,90,177],"(KD)":[2],"transfers":[3],"knowledge":[4,43,103,133],"from":[5,97],"large":[6],"teacher":[7,37,118,162],"models":[8],"to":[9,104,131,185],"compact":[10],"student":[11,84,158,163],"models,":[12],"enabling":[13],"efficient":[14,203],"deployment":[15],"on":[16,116],"resource":[17,202],"constrained":[18],"devices.":[19],"While":[20],"diverse":[21,161],"KD":[22,78,208],"methods,":[23],"including":[24],"response":[25],"based,":[26,28],"feature":[27],"and":[29,55,135,144,165,178,201],"relation":[30],"based":[31,115,190],"approaches,":[32],"capture":[33],"different":[34],"aspects":[35],"of":[36],"knowledge,":[38],"integrating":[39,206],"multiple":[40],"methods":[41],"or":[42],"sources":[44],"is":[45,85,198],"promising":[46],"but":[47],"often":[48],"hampered":[49],"by":[50],"complex":[51],"implementation,":[52],"inflexible":[53],"combinations,":[54,167],"catastrophic":[56],"forgetting,":[57],"which":[58],"limits":[59],"practical":[60,200],"effectiveness.":[61],"This":[62],"work":[63],"proposes":[64],"SMSKD":[65,139,155,197],"(Sequential":[66],"Multi":[67],"Stage":[68],"Distillation),":[70],"a":[71,88,93,199],"flexible":[72],"framework":[73],"that":[74,123,154,174],"sequentially":[75],"integrates":[76],"heterogeneous":[77,207],"methods.":[79,209],"At":[80],"each":[81],"stage,":[82],"the":[83,98,117,126],"trained":[86],"with":[87,147,188],"specific":[89],"method,":[91],"while":[92],"frozen":[94],"reference":[95,127,179],"model":[96,180],"previous":[99],"stage":[100,145,175],"anchors":[101],"learned":[102],"mitigate":[105],"forgetting.":[106],"In":[107],"addition,":[108],"we":[109],"introduce":[110],"an":[111],"adaptive":[112,191],"weighting":[113,192],"mechanism":[114],"true":[119],"class":[120],"probability":[121],"(TCP)":[122],"dynamically":[124],"adjusts":[125],"loss":[128],"per":[129],"sample":[130],"balance":[132],"retention":[134],"integration.":[136],"By":[137],"design,":[138],"supports":[140],"arbitrary":[141],"method":[142,166],"combinations":[143],"counts":[146],"negligible":[148],"computational":[149],"overhead.":[150],"Extensive":[151],"experiments":[152],"show":[153],"consistently":[156],"improves":[157],"accuracy":[159],"across":[160],"architectures":[164],"outperforming":[168],"existing":[169],"baselines.":[170],"Ablation":[171],"studies":[172],"confirm":[173],"wise":[176],"supervision":[181],"are":[182],"primary":[183],"contributors":[184],"performance":[186],"gains,":[187],"TCP":[189],"providing":[193],"complementary":[194],"benefits.":[195],"Overall,":[196],"solution":[204],"for":[205]},"counts_by_year":[],"updated_date":"2026-01-24T23:27:35.965710","created_date":"2026-01-24T00:00:00"}