{"id":"https://openalex.org/W7204171241","doi":"https://doi.org/10.18653/2026.emnlp-main.2031","title":"The Collaboration Tax: How Much LLM Multi-Agent Systems Pay to Coordinate","display_name":"The Collaboration Tax: How Much LLM Multi-Agent Systems Pay to Coordinate","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7204171241","doi":"https://doi.org/10.18653/2026.emnlp-main.2031"},"language":"en","primary_location":{"id":"doi:10.18653/2026.emnlp-main.2031","doi":"https://doi.org/10.18653/2026.emnlp-main.2031","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.2031","pdf_url":"https://aclanthology.org/2026.emnlp-main.2031.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://aclanthology.org/2026.emnlp-main.2031.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5148097372","display_name":"Weixiang Sun","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weixiang Sun","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006927984","display_name":"Zehong Wang","orcid":"https://orcid.org/0000-0002-9236-3954","observed_orcids":["https://orcid.org/0000-0002-9236-3954"]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zehong Wang","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023265280","display_name":"Hong Huang","orcid":"https://orcid.org/0000-0002-8957-4881","observed_orcids":["https://orcid.org/0000-0002-8957-4881"]},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Hong Huang","raw_affiliation_strings":["Simon Fraser University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Simon Fraser University","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031915304","display_name":"Colby Nelson","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Colby Nelson","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124835291","display_name":"Yanfang Ye","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yijun Ma","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5152623277","display_name":"Yanfang Ye","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanfang Ye","raw_affiliation_strings":["University of Notre Dame"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Notre Dame","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.7486,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.93392772,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"46258","last_page":"46281"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10456","display_name":"Multi-Agent Systems and Negotiation","score":0.5928000211715698,"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/T10456","display_name":"Multi-Agent Systems and Negotiation","score":0.5928000211715698,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.08240000158548355,"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":0.038100000470876694,"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/multi-agent-llm-systems","display_name":"multi-agent LLM systems","score":0.8830000162124634},{"id":"https://openalex.org/keywords/agent-coordination","display_name":"agent coordination","score":0.5659999847412109}],"concepts":[{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6534000039100647},{"id":"https://openalex.org/C2780069185","wikidata":"https://www.wikidata.org/wiki/Q7977945","display_name":"Equivalence (formal languages)","level":2,"score":0.585099995136261},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.5370000004768372},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5361999869346619},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4081999957561493},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.3889999985694885},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.38659998774528503},{"id":"https://openalex.org/C2780665704","wikidata":"https://www.wikidata.org/wiki/Q959298","display_name":"Intervention (counseling)","level":2,"score":0.3799999952316284},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.37959998846054077},{"id":"https://openalex.org/C153517567","wikidata":"https://www.wikidata.org/wiki/Q26090","display_name":"Mechanism design","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.34150001406669617},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C139676723","wikidata":"https://www.wikidata.org/wiki/Q1193832","display_name":"Sign (mathematics)","level":2,"score":0.3165999948978424},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.31610000133514404},{"id":"https://openalex.org/C2777742833","wikidata":"https://www.wikidata.org/wiki/Q1964083","display_name":"Reciprocal","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.31150001287460327},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2953999936580658},{"id":"https://openalex.org/C2780829048","wikidata":"https://www.wikidata.org/wiki/Q1624720","display_name":"Conversation analysis","level":3,"score":0.2782000005245209},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.27639999985694885},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.27459999918937683},{"id":"https://openalex.org/C2992972582","wikidata":"https://www.wikidata.org/wiki/Q8161","display_name":"Tax administration","level":3,"score":0.27390000224113464},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.26669999957084656},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.2648000121116638},{"id":"https://openalex.org/C100001284","wikidata":"https://www.wikidata.org/wiki/Q2248246","display_name":"Public economics","level":1,"score":0.2574000060558319},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.2500999867916107}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/2026.emnlp-main.2031","doi":"https://doi.org/10.18653/2026.emnlp-main.2031","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.2031","pdf_url":"https://aclanthology.org/2026.emnlp-main.2031.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2608.22152","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.22152","pdf_url":"https://arxiv.org/pdf/2608.22152","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.22152","doi":"https://doi.org/10.48550/arxiv.2608.22152","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.22152","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,"listed_in":[],"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"}],"best_oa_location":{"id":"doi:10.18653/2026.emnlp-main.2031","doi":"https://doi.org/10.18653/2026.emnlp-main.2031","is_oa":true,"landing_page_url":"https://doi.org/10.18653/2026.emnlp-main.2031","pdf_url":"https://aclanthology.org/2026.emnlp-main.2031.pdf","source":{"id":"https://openalex.org/S4363608991","display_name":"Conference on Empirical Methods in Natural Language Processing (EMNLP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204171241.pdf","grobid_xml":"https://content.openalex.org/works/W7204171241.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W4393100107","https://openalex.org/W4401023556","https://openalex.org/W4404346614","https://openalex.org/W4414903627","https://openalex.org/W4415967377","https://openalex.org/W4416020721","https://openalex.org/W7104028931","https://openalex.org/W7127092014","https://openalex.org/W7129073451","https://openalex.org/W7133224114","https://openalex.org/W7153098719","https://openalex.org/W7161053293","https://openalex.org/W7164535071","https://openalex.org/W7164589057","https://openalex.org/W7202257980"],"related_works":["https://openalex.org/W7153098719","https://openalex.org/W7168295175","https://openalex.org/W7222787858","https://openalex.org/W7163388633","https://openalex.org/W7213780296","https://openalex.org/W7202122171","https://openalex.org/W4414973927","https://openalex.org/W7164356780","https://openalex.org/W7150939889","https://openalex.org/W7168268051"],"abstract_inverted_index":{"Multi-agent":[0],"systems":[1,191],"built":[2],"from":[3,75,134],"large":[4],"language":[5],"models":[6,74],"are":[7],"deployed":[8],"widely,":[9],"yet":[10],"how":[11],"much":[12],"performance":[13],"is":[14,79,99,131,165],"lost":[15],"when":[16],"two":[17,44,82],"LLMs":[18],"must":[19],"coordinate":[20],"rather":[21,171],"than":[22,172],"act":[23],"alone":[24],"remains":[25],"unclear.We":[26],"formulate":[27],"the":[28,32,118,126,152,155,163,168,173,178],"collaboration":[29,188],"tax":[30,78,130,164],"as":[31,192],"team-decentralisation":[33],"loss":[34],"of":[35,66,151],"a":[36,53,85,92,101,105,140,148,193],"two-player":[37],"cooperative":[38],"game":[39],"with":[40,43,95,154],"private":[41],"information,":[42],"propositions":[45],"characterising":[46],"its":[47,50],"sign":[48],"and":[49,69,91,124,137,196],"equivalence":[51],"to":[52,116],"max-superadditivity":[54,179],"violation.We":[55],"operationalise":[56],"this":[57],"definition":[58],"on":[59,72],"32":[60],"solo-tractable":[61],"tasks":[62],"grouped":[63],"by":[64,182],"source":[65],"grounding":[67],"friction":[68],"measure":[70],"it":[71],"11":[73],"7":[76],"providers.The":[77],"structured":[80],"along":[81],"no-exception":[83],"axes:":[84],"category":[86],"ordering":[87],"across":[88,159],"every":[89],"model":[90],"monotonic":[93],"decrease":[94],"capability.The":[96],"proximate":[97],"mechanism":[98],"not":[100],"reasoning":[102],"deficit":[103],"but":[104],"four-stage":[106],"conversational":[107],"cascade":[108],"in":[109,189],"which":[110],"agents":[111],"make":[112],"ungrounded":[113],"claims,":[114],"fail":[115],"query":[117],"partner,":[119],"skip":[120],"integrating":[121],"both":[122],"views,":[123],"accept":[125],"answer":[127],"without":[128],"re-derivation.The":[129],"mechanically":[132],"predictable":[133],"conversation":[135],"features":[136],"partly":[138,197],"tractable:":[139],"prompt":[141],"intervention":[142],"targeting":[143],"all":[144],"four":[145],"stages":[146],"closes":[147],"substantial":[149],"fraction":[150],"gap,":[153],"dominant":[156],"bottleneck":[157],"differing":[158],"categories.In":[160],"heterogeneous":[161],"pairs":[162],"pulled":[166],"toward":[167],"stronger":[169],"partner":[170],"additive":[174],"midpoint,":[175],"empirically":[176],"realising":[177],"violation":[180],"predicted":[181],"our":[183],"framework.Together":[184],"these":[185],"results":[186],"recast":[187],"LLM":[190],"measurable,":[194],"predictable,":[195],"tractable":[198],"cost.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-08-26T00:00:00"}