{"id":"https://openalex.org/W7134233564","doi":"https://doi.org/10.48550/arxiv.2603.06003","title":"EvoESAP: Non-Uniform Expert Pruning for Sparse MoE","display_name":"EvoESAP: Non-Uniform Expert Pruning for Sparse MoE","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7134233564","doi":"https://doi.org/10.48550/arxiv.2603.06003"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.06003","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"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":"https://openalex.org/A5128389649","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/A5128448901","display_name":"Boyang Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Boyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128535751","display_name":"Zhiqiang Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Zhiqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128518152","display_name":"Xin Yuan","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17992674,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.3093999922275543,"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":0.3093999922275543,"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":0.1582999974489212,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.10520000010728836,"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/pruning","display_name":"Pruning","score":0.8256000280380249},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6449999809265137},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5713000297546387},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5390999913215637},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.38530001044273376},{"id":"https://openalex.org/keywords/order","display_name":"Order (exchange)","score":0.382999986410141},{"id":"https://openalex.org/keywords/expert-system","display_name":"Expert system","score":0.3813000023365021}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.8256000280380249},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6449999809265137},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6345999836921692},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5713000297546387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5625},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5390999913215637},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5026999711990356},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.38530001044273376},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.382999986410141},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.3813000023365021},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29429998993873596},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.27810001373291016},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2678999900817871}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.06003","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2603.06003","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.06003","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"pmh:doi:10.48550/arxiv.2603.06003","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"publisher-specific-oa","license_id":"https://openalex.org/licenses/publisher-specific-oa","version":"submittedVersion","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":{"Sparse":[0],"Mixture-of-Experts":[1],"(SMoE)":[2],"language":[3],"models":[4],"achieve":[5],"strong":[6],"capability":[7],"at":[8,163,182,194,201],"low":[9],"per-token":[10],"compute,":[11],"yet":[12],"deployment":[13],"remains":[14],"constrained":[15],"by":[16],"memory":[17],"footprint":[18],"and":[19,30,50,74,78,107,157,165],"throughput":[20],"because":[21],"the":[22,60,97,140,195],"full":[23,98],"expert":[24,33,72],"pool":[25],"must":[26],"still":[27],"be":[28],"stored":[29],"served.":[31],"Post-training":[32],"pruning":[34,69,142,193],"reduces":[35],"this":[36],"cost,":[37],"but":[38],"most":[39],"methods":[40],"focus":[41],"on":[42,116,180],"which":[43],"experts":[44],"to":[45,52,178],"prune":[46],"within":[47],"each":[48],"layer":[49],"default":[51],"a":[53,85,93,134,147],"uniform":[54,192],"layer-wise":[55,61,130],"sparsity":[56,131],"allocation,":[57,77],"even":[58],"though":[59],"allocation":[62,132],"can":[63],"strongly":[64],"affect":[65],"performance.":[66],"We":[67],"decouple":[68],"into":[70],"within-layer":[71,141],"ranking":[73],"across-layer":[75],"budget":[76,137],"introduce":[79],"\\textbf{E}xpected":[80],"\\textbf{S}peculative":[81],"\\textbf{A}cceptance":[82],"\\textbf{P}roxy":[83],"(\\textbf{ESAP}),":[84],"speculative-decoding-inspired,":[86],"teacher-forced":[87],"metric":[88],"that":[89,125,173],"measures":[90],"how":[91],"well":[92],"pruned":[94],"model":[95,99],"matches":[96],"without":[100],"costly":[101],"autoregressive":[102],"decoding.":[103],"ESAP":[104],"is":[105,199],"bounded":[106],"stable,":[108],"enabling":[109],"cheap":[110],"comparison":[111],"of":[112],"many":[113],"candidates.":[114],"Building":[115],"ESAP,":[117],"we":[118],"propose":[119],"EvoESAP,":[120],"an":[121,127],"evolutionary":[122],"search":[123],"framework":[124],"finds":[126],"improved":[128],"non-uniform":[129,171],"under":[133],"fixed":[135],"global":[136],"while":[138,185],"holding":[139],"order":[143],"fixed,":[144],"making":[145],"it":[146],"plug-and-play":[148],"method":[149],"for":[150],"criteria":[151],"such":[152],"as":[153],"Frequency,":[154],"EAN,":[155],"SEER,":[156],"REAP.":[158],"Across":[159],"7B--30B":[160],"SMoE":[161],"LLMs":[162],"25\\%":[164],"50\\%":[166,183],"sparsity,":[167],"EvoESAP":[168],"consistently":[169],"discovers":[170],"allocations":[172],"improve":[174],"open-ended":[175],"generation":[176],"(up":[177],"\\textbf{+19.6\\%}":[179],"MATH-500":[181],"sparsity)":[184],"preserving":[186],"competitive":[187],"multiple-choice":[188],"accuracy":[189],"compared":[190],"with":[191],"same":[196],"sparsity.":[197],"Code":[198],"available":[200],"https://github.com/ZongfangLiu/EvoESAP.":[202]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-10T00:00:00"}