{"id":"https://openalex.org/W7157077810","doi":"https://doi.org/10.48550/arxiv.2604.22849","title":"R$^3$AG: Retriever Routing for Retrieval-Augmented Generation","display_name":"R$^3$AG: Retriever Routing for Retrieval-Augmented Generation","publication_year":2026,"publication_date":"2026-04-22","ids":{"openalex":"https://openalex.org/W7157077810","doi":"https://doi.org/10.48550/arxiv.2604.22849"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.22849","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22849","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.2604.22849","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134795060","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/A5134780141","display_name":"Yutao Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yutao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134807516","display_name":"Yucheng Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Yucheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134802516","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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.829800009727478,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.829800009727478,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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":0.11590000241994858,"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":0.01679999940097332,"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/generator","display_name":"Generator (circuit theory)","score":0.6543999910354614},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.6470000147819519},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5210999846458435},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.42969998717308044},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4011000096797943},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.3515999913215637}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7526999711990356},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.6543999910354614},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.6470000147819519},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5210999846458435},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.42969998717308044},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32600000500679016},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3003999888896942},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2937000095844269},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.2800000011920929},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C104954878","wikidata":"https://www.wikidata.org/wiki/Q1648707","display_name":"Routing protocol","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C2983435990","wikidata":"https://www.wikidata.org/wiki/Q22725","display_name":"Network routing","level":3,"score":0.2581999897956848},{"id":"https://openalex.org/C2778180026","wikidata":"https://www.wikidata.org/wiki/Q18378163","display_name":"Semantic heterogeneity","level":4,"score":0.2547000050544739},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.22849","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22849","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.2604.22849","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22849","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":{"Retrieval-augmented":[0],"generation":[1,121],"(RAG)":[2],"has":[3],"become":[4],"a":[5,47,62,67,92,125],"cornerstone":[6],"for":[7,29],"knowledge-intensive":[8,150],"tasks.":[9],"However,":[10],"the":[11,19,39,79,99,158],"efficacy":[12],"of":[13],"RAG":[14],"is":[15],"often":[16],"bottlenecked":[17],"by":[18],"``one-size-fits-all''":[20],"retrieval":[21,118],"paradigm,":[22],"as":[23],"different":[24,30],"queries":[25,103],"exhibit":[26],"distinct":[27],"preferences":[28],"retrievers.":[31],"While":[32],"recent":[33],"routing":[34,94,165],"techniques":[35],"attempt":[36],"to":[37,141],"select":[38],"optimal":[40],"retriever":[41,105,112],"dynamically,":[42],"they":[43],"typically":[44],"operate":[45],"under":[46],"``single":[48],"and":[49,104,120,137,162],"static":[50,164],"capability''":[51],"assumption,":[52],"selecting":[53],"retrievers":[54,161],"solely":[55],"based":[56],"on":[57,148],"semantic":[58],"relevance.":[59],"This":[60],"overlooks":[61],"critical":[63],"distinction":[64],"in":[65,81],"RAG:":[66],"retrieved":[68],"document":[69,135],"must":[70],"not":[71],"only":[72],"be":[73],"relevant":[74],"but":[75],"also":[76],"effectively":[77],"support":[78],"generator":[80],"producing":[82],"correct":[83],"answers.":[84],"To":[85],"address":[86],"this":[87],"limitation,":[88],"we":[89],"propose":[90],"R$^3$AG,":[91],"novel":[93],"framework":[95],"that":[96,129,153],"explicitly":[97],"models":[98],"dynamic":[100],"alignment":[101],"between":[102],"capabilities.":[106],"Unlike":[107],"previous":[108],"approaches,":[109],"R$^3$AG":[110,154],"decomposes":[111],"capability":[113],"into":[114],"two":[115],"learnable":[116],"dimensions:":[117],"quality":[119],"utility.":[122],"We":[123],"employ":[124],"contrastive":[126],"learning":[127],"objective":[128],"leverages":[130],"complementary":[131],"supervision":[132],"signals,":[133],"\\textit{i.e.},":[134],"assessments":[136],"downstream":[138],"answer":[139],"correctness,":[140],"capture":[142],"query-specific":[143],"preference":[144],"shifts.":[145],"Extensive":[146],"experiments":[147],"several":[149],"tasks":[151],"show":[152],"consistently":[155],"outperforms":[156],"both":[157],"best":[159],"individual":[160],"state-of-the-art":[163],"methods.":[166]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-29T00:00:00"}