{"id":"https://openalex.org/W7139937914","doi":"https://doi.org/10.48550/arxiv.2603.18492","title":"AIMER: Calibration-Free Task-Agnostic MoE Expert Pruning","display_name":"AIMER: Calibration-Free Task-Agnostic MoE Expert Pruning","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7139937914","doi":"https://doi.org/10.48550/arxiv.2603.18492"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.18492","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18492","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.2603.18492","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5072076736","display_name":"Zhonghua Liu","orcid":"https://orcid.org/0000-0002-0375-206X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zongfang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Guangyi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Guangyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130214493","display_name":"Shengkun Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Shengkun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130230231","display_name":"Yifan Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130244838","display_name":"Huan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Huan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Yuan, Xin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Xin","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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.2667999863624573,"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"}},"topics":[{"id":"https://openalex.org/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.2667999863624573,"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/T10028","display_name":"Topic Modeling","score":0.23759999871253967,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.15039999783039093,"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/pruning","display_name":"Pruning","score":0.8747000098228455},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.6050000190734863},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4968999922275543},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4375},{"id":"https://openalex.org/keywords/expert-system","display_name":"Expert system","score":0.36250001192092896},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.3425000011920929}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8747000098228455},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6050000190734863},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.57669997215271},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4968999922275543},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4749999940395355},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4375},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4101000130176544},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4043999910354614},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3840000033378601},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3425000011920929},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2603999972343445}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.18492","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18492","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.2603.18492","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18492","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Mixture-of-Experts":[0],"(MoE)":[1],"language":[2,105],"models":[3,106],"increase":[4],"parameter":[5],"capacity":[6],"without":[7],"proportional":[8],"per-token":[9],"computation,":[10],"yet":[11],"deployment":[12],"still":[13],"requires":[14],"storing":[15],"the":[16,86,140],"full":[17],"expert":[18,21,39,90,98],"pool,":[19],"making":[20,50,92],"pruning":[22,51],"important":[23],"for":[24,72,96],"reducing":[25],"memory":[26],"and":[27,110],"serving":[28],"overhead.":[29],"Existing":[30],"task-agnostic":[31,97,143],"expert-pruning":[32,136],"methods":[33],"are":[34],"typically":[35],"calibration-dependent:":[36],"they":[37],"estimate":[38],"importance":[40],"from":[41],"routing":[42],"or":[43],"activation":[44],"statistics":[45],"on":[46,139],"a":[47,75],"calibration":[48],"set,":[49],"decisions":[52],"sensitive":[53],"to":[54,102,151],"calibration-data":[55],"variation":[56],"while":[57,146],"introducing":[58],"substantial":[59],"preprocessing":[60],"cost.":[61],"We":[62],"propose":[63],"AIMER":[64,114,128],"(\\textbf{A}bsolute":[65],"mean":[66,69],"over":[67],"root":[68],"square":[70],"\\textbf{IM}portance":[71],"\\textbf{E}xpert":[73],"\\textbf{R}anking),":[74],"simple":[76],"calibration-free":[77,125],"criterion":[78],"that":[79],"identifies":[80],"more":[81],"distinct":[82,108],"experts":[83],"by":[84],"capturing":[85],"concentration":[87],"pattern":[88],"of":[89],"weights,":[91],"it":[93],"well":[94],"suited":[95],"pruning.":[99],"Across":[100],"7B":[101],"47B":[103],"MoE":[104],"with":[107],"architectures":[109],"16":[111],"diverse":[112,121],"benchmarks,":[113],"consistently":[115],"delivers":[116],"stronger":[117],"capability":[118],"balance":[119,132],"across":[120],"tasks":[122],"than":[123,133],"existing":[124],"methods.":[126],"Surprisingly,":[127],"also":[129],"achieves":[130],"better":[131],"strong":[134],"calibration-based":[135],"baselines":[137],"calibrated":[138],"widely":[141],"used":[142],"C4":[144],"corpus,":[145],"requiring":[147],"only":[148],"0.22--2.06":[149],"seconds":[150],"score":[152],"all":[153],"experts.":[154]},"counts_by_year":[],"updated_date":"2026-07-21T08:15:58.654021","created_date":"2026-03-21T00:00:00"}