{"id":"https://openalex.org/W7165203696","doi":"https://doi.org/10.48550/arxiv.2606.18304","title":"Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression","display_name":"Attribution-Guided and Coverage-Maximized Pruning for Structural MoE Compression","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7165203696","doi":"https://doi.org/10.48550/arxiv.2606.18304"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.18304","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18304","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.18304","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138872613","display_name":"Yifu Ding","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Yifu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138895876","display_name":"Jiacheng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiacheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138915697","display_name":"Ge Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Ge","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138844697","display_name":"Yongcheng Jing","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing, Yongcheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138962646","display_name":"Jinyang Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Jinyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138857306","display_name":"Xianglong Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xianglong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138875895","display_name":"Dacheng Tao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tao, Dacheng","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":0.3043999969959259,"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":0.3043999969959259,"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":0.10019999742507935,"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":0.04600000008940697,"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":0.7523000240325928},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5857999920845032},{"id":"https://openalex.org/keywords/memory-footprint","display_name":"Memory footprint","score":0.508899986743927},{"id":"https://openalex.org/keywords/compression","display_name":"Compression (physics)","score":0.5012000203132629},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4982999861240387},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4796999990940094}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.7523000240325928},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6565999984741211},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5857999920845032},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.508899986743927},{"id":"https://openalex.org/C180016635","wikidata":"https://www.wikidata.org/wiki/Q2712821","display_name":"Compression (physics)","level":2,"score":0.5012000203132629},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4982999861240387},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4936999976634979},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4796999990940094},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.427700012922287},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4205999970436096},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3763999938964844},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37369999289512634},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2703000009059906},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.260699987411499}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.18304","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18304","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.18304","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18304","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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],"models":[2,127],"scale":[3],"compute":[4],"efficiently,":[5],"yet":[6],"remain":[7],"expensive":[8],"to":[9,12,48,53],"deploy":[10],"due":[11],"their":[13],"substantial":[14,81],"memory":[15,151],"footprint":[16,152],"and":[17,57,113,124,155],"inference":[18],"overhead.":[19],"Prior":[20],"compression":[21],"methods":[22],"mainly":[23],"operate":[24],"at":[25],"the":[26],"expert":[27],"level,":[28],"either":[29],"removing":[30],"entire":[31],"experts":[32,35,70,85],"or":[33,137],"ranking":[34],"by":[36,153],"coarse-grained":[37],"importance":[38],"scores.":[39],"However,":[40],"such":[41],"expert-wise":[42],"decisions":[43],"are":[44],"often":[45],"too":[46],"coarse":[47],"capture":[49],"fine-grained":[50],"redundancy,":[51],"leading":[52],"misallocated":[54],"pruning":[55,96,140],"budgets":[56],"limited":[58],"compression.":[59],"To":[60],"address":[61],"this":[62,90],"problem,":[63],"we":[64,92],"observe":[65],"that":[66,129],"information":[67],"within":[68],"MoE":[69,100,126],"is":[71],"highly":[72],"concentrated":[73],"in":[74,84],"a":[75,94,108],"small":[76],"subset":[77],"of":[78],"channels,":[79],"leaving":[80],"redundancy":[82],"even":[83],"deemed":[86],"important.":[87],"Based":[88],"on":[89,122],"observation,":[91],"propose":[93],"structural":[95],"framework":[97],"tailored":[98],"for":[99],"models.":[101],"Our":[102],"method":[103,131],"reformulates":[104],"prune-ratio":[105],"allocation":[106],"as":[107],"channel-score":[109],"coverage":[110],"maximization":[111],"problem":[112],"solves":[114],"it":[115],"efficiently":[116],"using":[117],"an":[118],"attribution-based":[119],"approximation.":[120],"Experiments":[121],"DeepSeek":[123],"Qwen":[125],"show":[128],"our":[130,148],"preserves":[132],"model":[133],"accuracy":[134],"under":[135],"50%":[136],"25%":[138],"structured":[139],"when":[141],"combined":[142],"with":[143],"4-bit":[144],"quantization.":[145],"On":[146],"Qwen3-30B-A3B,":[147],"approach":[149],"reduces":[150],"5.27$\\times$":[154],"consistently":[156],"outperforms":[157],"state-of-the-art":[158],"baselines":[159],"across":[160],"diverse":[161],"benchmarks.":[162]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-19T00:00:00"}