{"id":"https://openalex.org/W7137282234","doi":"https://doi.org/10.48550/arxiv.2603.12645","title":"LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing","display_name":"LightMoE: Reducing Mixture-of-Experts Redundancy through Expert Replacing","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7137282234","doi":"https://doi.org/10.48550/arxiv.2603.12645"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.12645","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12645","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.2603.12645","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129628123","display_name":"Jiawei Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Jiawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129572230","display_name":"Zhiwei Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Zhiwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129443626","display_name":"Jianyuan Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Jianyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129556409","display_name":"Li Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129420708","display_name":"Yong Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Yong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129422685","display_name":"Han Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Han","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129599440","display_name":"Dan Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Dan","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":0.43309998512268066,"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":0.43309998512268066,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.1137000024318695,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.066600002348423,"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/redundancy","display_name":"Redundancy (engineering)","score":0.6316999793052673},{"id":"https://openalex.org/keywords/expert-system","display_name":"Expert system","score":0.5274999737739563},{"id":"https://openalex.org/keywords/pruning","display_name":"Pruning","score":0.474700003862381},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.4007999897003174},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.39419999718666077},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.35679998993873596}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7785000205039978},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6316999793052673},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.550599992275238},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.5274999737739563},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4821999967098236},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.474700003862381},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4007999897003174},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.35679998993873596},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.287200003862381},{"id":"https://openalex.org/C105002631","wikidata":"https://www.wikidata.org/wiki/Q4833645","display_name":"Subject-matter expert","level":3,"score":0.28279998898506165},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27869999408721924},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2671999931335449}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.12645","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12645","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.2603.12645","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.12645","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":{"Mixture-of-Experts":[0],"(MoE)":[1],"based":[2],"Large":[3],"Language":[4],"Models":[5],"(LLMs)":[6],"have":[7],"demonstrated":[8],"impressive":[9],"performance":[10,132,156],"and":[11,78,120,153,178],"computational":[12],"efficiency.":[13],"However,":[14],"their":[15,80],"deployment":[16],"is":[17],"often":[18,48],"constrained":[19],"by":[20,112],"substantial":[21],"memory":[22,174],"demands,":[23],"primarily":[24],"due":[25],"to":[26,29,44],"the":[27,110,131],"need":[28],"load":[30],"numerous":[31],"expert":[32,36,65,69,115,118],"modules.":[33],"While":[34],"existing":[35,151],"compression":[37,66,139,147],"techniques":[38],"like":[39],"pruning":[40],"or":[41,54],"merging":[42],"attempt":[43],"mitigate":[45],"this,":[46],"they":[47],"suffer":[49],"from":[50],"irreversible":[51],"knowledge":[52],"loss":[53],"high":[55],"training":[56,84,176],"overhead.":[57],"In":[58],"this":[59,94,101],"paper,":[60],"we":[61,103],"propose":[62],"a":[63,90,106,137,143,170],"novel":[64],"paradigm":[67,95,111],"termed":[68],"replacing,":[70],"which":[71],"replaces":[72],"redundant":[73],"experts":[74],"with":[75,82],"parameter-efficient":[76],"modules":[77],"recovers":[79],"capabilities":[81],"low":[83],"costs.":[85],"We":[86],"find":[87],"that":[88,108,128,167],"even":[89],"straightforward":[91],"baseline":[92],"of":[93,133,158],"yields":[96],"promising":[97],"performance.":[98,180],"Building":[99],"on":[100],"foundation,":[102],"introduce":[104],"LightMoE,":[105],"framework":[107],"enhances":[109],"introducing":[113],"adaptive":[114],"selection,":[116],"hierarchical":[117],"construction,":[119],"an":[121],"annealed":[122],"recovery":[123],"strategy.":[124],"Experimental":[125],"results":[126],"show":[127],"LightMoE":[129,168],"matches":[130],"LoRA":[134],"fine-tuning":[135],"at":[136],"30%":[138],"ratio.":[140],"Even":[141],"under":[142],"more":[144],"aggressive":[145],"50%":[146],"rate,":[148],"it":[149],"outperforms":[150],"methods":[152],"achieves":[154],"average":[155],"improvements":[157],"5.6%":[159],"across":[160],"five":[161],"diverse":[162],"tasks.":[163],"These":[164],"findings":[165],"demonstrate":[166],"strikes":[169],"superior":[171],"balance":[172],"among":[173],"efficiency,":[175,177],"model":[179]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-17T00:00:00"}