{"id":"https://openalex.org/W7155524307","doi":"https://doi.org/10.48550/arxiv.2604.21794","title":"Learning to Communicate: Toward End-to-End Optimization of Multi-Agent Language Systems","display_name":"Learning to Communicate: Toward End-to-End Optimization of Multi-Agent Language Systems","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7155524307","doi":"https://doi.org/10.48550/arxiv.2604.21794"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.21794","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21794","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.2604.21794","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134551977","display_name":"Ye Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Ye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017492492","display_name":"Heming Liu","orcid":"https://orcid.org/0009-0007-3400-5925"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Heming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134563099","display_name":"Haibo Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Haibo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134509965","display_name":"Xiaopeng Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Xiaopeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134553816","display_name":"Peng Kuang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kuang, Peng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134557028","display_name":"Haohan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haohan","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/T10028","display_name":"Topic Modeling","score":0.29089999198913574,"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/T10028","display_name":"Topic Modeling","score":0.29089999198913574,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2556999921798706,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.11259999871253967,"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/commonsense-reasoning","display_name":"Commonsense reasoning","score":0.6054999828338623},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.6003000140190125},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.5126000046730042},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.510699987411499},{"id":"https://openalex.org/keywords/orchestration","display_name":"Orchestration","score":0.48989999294281006},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4058000147342682},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.3977000117301941}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.760699987411499},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.6054999828338623},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.6003000140190125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5946999788284302},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.5126000046730042},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.510699987411499},{"id":"https://openalex.org/C199168358","wikidata":"https://www.wikidata.org/wiki/Q3367000","display_name":"Orchestration","level":3,"score":0.48989999294281006},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41600000858306885},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4058000147342682},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.38449999690055847},{"id":"https://openalex.org/C158156997","wikidata":"https://www.wikidata.org/wiki/Q1416645","display_name":"Models of communication","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C101765175","wikidata":"https://www.wikidata.org/wiki/Q577764","display_name":"Communications system","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31200000643730164},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C32254414","wikidata":"https://www.wikidata.org/wiki/Q4724364","display_name":"Algorithmic learning theory","level":3,"score":0.25870001316070557},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2565000057220459}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.21794","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21794","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.2604.21794","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.21794","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":[{"display_name":"Quality Education","score":0.6330199837684631,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multi-agent":[0],"systems":[1],"built":[2],"on":[3,11,19,101,134,137],"large":[4],"language":[5],"models":[6],"have":[7],"shown":[8],"strong":[9],"performance":[10],"complex":[12],"reasoning":[13,116,143],"tasks,":[14],"yet":[15],"most":[16],"work":[17],"focuses":[18],"agent":[20],"roles":[21],"and":[22,96,108,118,127,139],"orchestration":[23],"while":[24],"treating":[25],"inter-agent":[26],"communication":[27,33,55,69,130],"as":[28,38,70],"a":[29,42,63,71],"fixed":[30],"interface.":[31],"Latent":[32],"through":[34],"internal":[35],"representations":[36],"such":[37],"key-value":[39],"caches":[40],"offers":[41],"promising":[43],"alternative":[44],"to":[45,88],"text-based":[46,124],"protocols,":[47],"but":[48],"existing":[49],"approaches":[50],"do":[51],"not":[52],"jointly":[53,89],"optimize":[54],"with":[56],"multi-agent":[57,75,83,125],"reasoning.":[58],"Therefore":[59],"we":[60],"propose":[61],"DiffMAS,":[62],"training":[64,81],"framework":[65],"that":[66,112],"treats":[67],"latent":[68,84,129],"learnable":[72],"component":[73],"of":[74],"systems.":[76],"DiffMAS":[77,113],"performs":[78],"parameter-efficient":[79],"supervised":[80],"over":[82,121],"trajectories,":[85],"enabling":[86],"agents":[87],"learn":[90],"how":[91],"information":[92],"should":[93],"be":[94],"encoded":[95],"interpreted":[97],"across":[98,142],"interactions.":[99],"Experiments":[100],"mathematical":[102],"reasoning,":[103],"scientific":[104],"QA,":[105],"code":[106],"generation,":[107],"commonsense":[109],"benchmarks":[110],"show":[111],"consistently":[114],"improves":[115],"accuracy":[117],"decoding":[119],"stability":[120],"single-agent":[122],"inference,":[123],"systems,":[126],"prior":[128],"methods,":[131],"achieving":[132],"26.7%":[133],"AIME24,":[135],"20.2%":[136],"GPQA-Diamond,":[138],"consistent":[140],"gains":[141],"benchmarks.":[144]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-25T00:00:00"}
