{"id":"https://openalex.org/W7151608107","doi":"https://doi.org/10.48550/arxiv.2604.04929","title":"Rethinking Model Efficiency: Multi-Agent Inference with Large Models","display_name":"Rethinking Model Efficiency: Multi-Agent Inference with Large Models","publication_year":2026,"publication_date":"2026-04-06","ids":{"openalex":"https://openalex.org/W7151608107","doi":"https://doi.org/10.48550/arxiv.2604.04929"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.04929","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04929","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.2604.04929","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018400002","display_name":"Sixun Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Sixun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029673876","display_name":"Juhua Hu","orcid":"https://orcid.org/0000-0001-5869-3549"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Juhua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133126317","display_name":"Steven Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Steven","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133121814","display_name":"Wei Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133140741","display_name":"Qi Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Qi","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8676999807357788,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8676999807357788,"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"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.022099999710917473,"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/T10028","display_name":"Topic Modeling","score":0.012000000104308128,"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/inference","display_name":"Inference","score":0.6869999766349792},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.635200023651123},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6269999742507935},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5030999779701233},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.5026999711990356},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4496000111103058}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7705000042915344},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6869999766349792},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.635200023651123},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6269999742507935},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.5026999711990356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46209999918937683},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4496000111103058},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41780000925064087},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.39500001072883606},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.04929","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04929","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.2604.04929","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04929","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":{"Most":[0],"vision-language":[1],"models":[2,38,139],"(VLMs)":[3],"apply":[4],"a":[5,56,74,86,90,105,115,132,177],"large":[6,75,106,128,138,178],"language":[7],"model":[8,76,88,107,117,152,179],"(LLM)":[9],"as":[10,114],"the":[11,14,23,30,33,60,102,125,145,150,164,174,186],"decoder,":[12],"where":[13],"response":[15],"tokens":[16,27,46,80,148,166],"are":[17],"generated":[18],"sequentially":[19],"through":[20],"autoregression.":[21],"Therefore,":[22],"number":[24],"of":[25,32,44,59,65,127,176,188],"output":[26,45,79,92,121],"can":[28,81,108,171],"be":[29,82],"bottleneck":[31],"end-to-end":[34],"latency.":[35],"However,":[36],"different":[37,42,63],"may":[39],"require":[40],"vastly":[41],"numbers":[43],"to":[47],"achieve":[48,109],"comparable":[49,112],"performance.":[50],"In":[51],"this":[52],"work,":[53],"we":[54,130],"conduct":[55],"comprehensive":[57],"analysis":[58],"latency":[61],"across":[62],"components":[64],"VLMs":[66],"on":[67,97,157],"simulated":[68],"data.":[69],"The":[70,94,155],"experiment":[71],"shows":[72],"that":[73,104,136,161],"with":[77,89,118,140,180],"fewer":[78,120],"more":[83],"efficient":[84],"than":[85],"small":[87,116,151,168],"long":[91],"sequence.":[93],"empirical":[95],"study":[96],"diverse":[98],"real-world":[99],"benchmarks":[100],"confirms":[101,185],"observation":[103],"better":[110],"or":[111],"performance":[113,175],"significantly":[119],"tokens.":[122],"To":[123],"leverage":[124],"efficiency":[126],"models,":[129,169],"propose":[131],"multi-agent":[133],"inference":[134],"framework":[135],"keeps":[137],"short":[141],"responses":[142],"but":[143],"transfers":[144],"key":[146],"reasoning":[147,165],"from":[149,167],"when":[153],"necessary.":[154],"comparison":[156],"benchmark":[158],"tasks":[159],"demonstrates":[160],"by":[162],"reusing":[163],"it":[170],"help":[172],"approach":[173],"its":[181],"own":[182],"reasoning,":[183],"which":[184],"effectiveness":[187],"our":[189],"proposal.":[190]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}