{"id":"https://openalex.org/W7162439103","doi":"https://doi.org/10.48550/arxiv.2605.24486","title":"AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning","display_name":"AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning","publication_year":2026,"publication_date":"2026-05-23","ids":{"openalex":"https://openalex.org/W7162439103","doi":"https://doi.org/10.48550/arxiv.2605.24486"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24486","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24486","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.2605.24486","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137070398","display_name":"Yuyang Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yuyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137077451","display_name":"Hongjin Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Hongjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137042971","display_name":"Shuting Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shuting","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137006480","display_name":"Jiongnan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jiongnan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137065891","display_name":"Tong Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Tong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137031393","display_name":"Xiaoxi Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Xiaoxi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137070496","display_name":"Zheng Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137058664","display_name":"Zhicheng Dou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dou, Zhicheng","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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.3711000084877014,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.3711000084877014,"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":0.10249999910593033,"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/T10456","display_name":"Multi-Agent Systems and Negotiation","score":0.09000000357627869,"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/workflow","display_name":"Workflow","score":0.6554999947547913},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6506999731063843},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.45829999446868896},{"id":"https://openalex.org/keywords/multi-agent-system","display_name":"Multi-agent system","score":0.42089998722076416},{"id":"https://openalex.org/keywords/autonomous-agent","display_name":"Autonomous agent","score":0.36309999227523804},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.3531000018119812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7131999731063843},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.6554999947547913},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6506999731063843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49470001459121704},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.45829999446868896},{"id":"https://openalex.org/C41550386","wikidata":"https://www.wikidata.org/wiki/Q529909","display_name":"Multi-agent system","level":2,"score":0.42089998722076416},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.41429999470710754},{"id":"https://openalex.org/C13687954","wikidata":"https://www.wikidata.org/wiki/Q4826847","display_name":"Autonomous agent","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3531000018119812},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.35089999437332153},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.3375000059604645},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.29010000824928284},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C100339178","wikidata":"https://www.wikidata.org/wiki/Q2548752","display_name":"Collective behavior","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C89057211","wikidata":"https://www.wikidata.org/wiki/Q432197","display_name":"Collective intelligence","level":2,"score":0.2637999951839447}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24486","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24486","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.2605.24486","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24486","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"progress":[1],"on":[2,51,89],"long-horizon":[3,157],"agentic":[4],"tasks":[5],"has":[6,93],"been":[7],"driven":[8],"largely":[9],"by":[10],"scaling":[11,31,174],"up":[12],"individual":[13],"agents":[14,77,108],"through":[15],"stronger":[16],"models,":[17],"better":[18],"tools,":[19],"and":[20,62,99,150],"more":[21,193],"effective":[22],"scaffolding.":[23],"In":[24],"contrast,":[25],"much":[26],"less":[27],"is":[28],"understood":[29],"about":[30],"out:":[32],"whether":[33],"multiple":[34],"peer":[35,76,176],"agents,":[36],"all":[37],"targeting":[38],"the":[39,79,84,139,155],"same":[40,80],"task,":[41],"can":[42,172],"become":[43],"an":[44],"additional":[45],"source":[46,182],"of":[47,129,183,191],"capability":[48,184],"without":[49,133],"relying":[50],"explicit":[52],"role":[53],"specialization":[54],"or":[55,96],"workflow":[56],"orchestration.":[57],"We":[58,137],"study":[59],"this":[60],"question":[61],"propose":[63],"AgentFugue,":[64],"a":[65,71,112,126,142,180,189],"collective":[66,170],"reasoning":[67,73,132,171],"framework":[68],"built":[69],"around":[70],"shared":[72],"hub.":[74],"As":[75],"explore":[78],"task":[81],"in":[82,111],"parallel,":[83],"hub":[85,140],"records":[86],"concise":[87],"notes":[88],"what":[90,106],"each":[91,101],"agent":[92,102,177],"established,":[94],"attempted,":[95],"ruled":[97],"out,":[98],"enables":[100],"to":[103],"selectively":[104],"access":[105],"other":[107],"have":[109],"discovered":[110],"form":[113],"useful":[114],"for":[115],"its":[116],"current":[117],"search.":[118],"This":[119],"design":[120],"turns":[121],"otherwise":[122],"isolated":[123],"trajectories":[124],"into":[125,179],"connected":[127],"ecology":[128],"reusable":[130],"intermediate":[131],"requiring":[134],"centralized":[135],"planning.":[136],"instantiate":[138],"as":[141],"plug-in":[143],"communication":[144],"layer,":[145],"trained":[146],"with":[147],"supervised":[148],"fine-tuning":[149],"end-to-end":[151],"reinforcement":[152],"learning.":[153],"Across":[154],"challenging":[156],"settings":[158],"we":[159],"study,":[160],"AgentFugue":[161],"improves":[162],"over":[163],"strong":[164],"baselines.":[165],"Our":[166],"results":[167],"suggest":[168],"that":[169],"turn":[173],"out":[175],"systems":[178],"distinct":[181],"gains,":[185],"rather":[186],"than":[187],"merely":[188],"way":[190],"spending":[192],"compute.":[194]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}