{"id":"https://openalex.org/W7161255858","doi":"https://doi.org/10.48550/arxiv.2605.14438","title":"BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE","display_name":"BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE","publication_year":2026,"publication_date":"2026-05-14","ids":{"openalex":"https://openalex.org/W7161255858","doi":"https://doi.org/10.48550/arxiv.2605.14438"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.14438","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14438","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.2605.14438","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123010208","display_name":"Juntong Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Juntong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125560904","display_name":"Jialiang Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Jialiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121322336","display_name":"Qishen Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Qishen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136228809","display_name":"Yue Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103928829","display_name":"Yuliang Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Yuliang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136205393","display_name":"Fuyu Lv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lv, Fuyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062155645","display_name":"Ou Dan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dan, Ou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136269459","display_name":"Li Yuan","orcid":"https://orcid.org/0000-0002-2120-5588"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Li","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":0.1762000024318695,"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":0.1762000024318695,"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":0.10440000146627426,"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":0.07930000126361847,"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/inference","display_name":"Inference","score":0.6455000042915344},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4699999988079071},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4429999887943268},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.421099990606308},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.41839998960494995},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.41510000824928284},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.3937000036239624},{"id":"https://openalex.org/keywords/minification","display_name":"Minification","score":0.392300009727478},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"Approximate inference","score":0.382099986076355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7107999920845032},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6455000042915344},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4699999988079071},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4429999887943268},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.43299999833106995},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.41839998960494995},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.41510000824928284},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3937000036239624},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.392300009727478},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.382099986076355},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3671000003814697},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34459999203681946},{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.3431999981403351},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C46743427","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference engine","level":3,"score":0.33489999175071716},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C24856439","wikidata":"https://www.wikidata.org/wiki/Q352483","display_name":"Adaptive routing","level":5,"score":0.3107999861240387},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30219998955726624},{"id":"https://openalex.org/C2775973920","wikidata":"https://www.wikidata.org/wiki/Q3252726","display_name":"Selection algorithm","level":3,"score":0.2953000068664551},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2930000126361847},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.29159998893737793},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.289900004863739},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.28189998865127563},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2619999945163727},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.2590999901294708},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.2515999972820282},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.14438","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14438","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.2605.14438","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.14438","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],"architectures":[2],"enhance":[3],"the":[4,117,129],"efficiency":[5],"of":[6,15,128],"large":[7],"language":[8],"models":[9],"by":[10,138],"activating":[11],"only":[12],"a":[13,23,70,83,156],"subset":[14],"experts":[16],"per":[17],"token.":[18],"However,":[19],"standard":[20],"MoE":[21,135,162],"employs":[22],"fixed":[24],"Top-K":[25],"routing":[26],"strategy,":[27],"leading":[28],"to":[29,56,140,144],"redundant":[30],"computation":[31],"and":[32,86,148],"suboptimal":[33],"inference":[34,119],"latency.":[35],"Existing":[36],"acceleration":[37],"methods":[38],"either":[39],"require":[40],"costly":[41],"retraining":[42],"with":[43,116],"architectural":[44],"changes":[45],"or":[46],"suffer":[47],"from":[48],"severe":[49],"performance":[50,132],"drop":[51],"at":[52],"high":[53],"sparsity":[54,95],"due":[55],"train-inference":[57],"mismatch.":[58],"To":[59],"address":[60],"these":[61],"limitations,":[62],"we":[63],"propose":[64],"BEAM":[65,91,124],"(Binary":[66],"Expert":[67],"Activation":[68],"Masking),":[69],"novel":[71],"method":[72],"that":[73,123],"learns":[74],"token-adaptive":[75],"expert":[76,94],"selection":[77],"via":[78],"trainable":[79],"binary":[80],"masks.":[81],"With":[82],"straight-through":[84],"estimator":[85],"an":[87,106],"auxiliary":[88],"regularization":[89],"loss,":[90],"induces":[92],"dynamic":[93],"through":[96],"end-to-end":[97],"training":[98],"while":[99,133],"maintaining":[100],"model":[101],"capability.":[102],"We":[103],"further":[104],"implement":[105],"efficient":[107,161],"custom":[108],"CUDA":[109],"kernel":[110],"for":[111,160],"BEAM,":[112],"ensuring":[113],"seamless":[114],"integration":[115],"vLLM":[118],"framework.":[120],"Experiments":[121],"show":[122],"retains":[125],"over":[126],"98\\%":[127],"original":[130],"model's":[131],"reducing":[134],"layer":[136],"FLOPs":[137],"up":[139,143],"85\\%,":[141],"achieving":[142],"2.5$\\times$":[145],"faster":[146],"decoding":[147],"1.4$\\times$":[149],"higher":[150],"throughput,":[151],"demonstrating":[152],"its":[153],"effectiveness":[154],"as":[155],"practical,":[157],"plug-and-play":[158],"solution":[159],"inference.":[163]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-16T00:00:00"}