{"id":"https://openalex.org/W7168399808","doi":"https://doi.org/10.1145/3805712.3809717","title":"Uncertainty Quantification for Retrieval-Augmented Reasoning","display_name":"Uncertainty Quantification for Retrieval-Augmented Reasoning","publication_year":2026,"publication_date":"2026-07-15","ids":{"openalex":"https://openalex.org/W7168399808","doi":"https://doi.org/10.1145/3805712.3809717"},"language":null,"primary_location":{"id":"doi:10.1145/3805712.3809717","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805712.3809717","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3805712.3809717","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140865597","display_name":"Heydar Soudani","orcid":"https://orcid.org/0000-0003-0393-8662"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Heydar Soudani","raw_affiliation_strings":["Radboud University, Nijmegen, Netherlands"],"raw_orcid":"https://orcid.org/0000-0003-0393-8662","affiliations":[{"raw_affiliation_string":"Radboud University, Nijmegen, Netherlands","institution_ids":["https://openalex.org/I145872427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140870383","display_name":"Hamed Zamani","orcid":"https://orcid.org/0000-0002-0800-3340"},"institutions":[{"id":"https://openalex.org/I24603500","display_name":"University of Massachusetts Amherst","ror":"https://ror.org/0072zz521","country_code":"US","type":"education","lineage":["https://openalex.org/I24603500"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hamed Zamani","raw_affiliation_strings":["University of Massachusetts Amherst, Amherst, USA"],"raw_orcid":"https://orcid.org/0000-0002-0800-3340","affiliations":[{"raw_affiliation_string":"University of Massachusetts Amherst, Amherst, USA","institution_ids":["https://openalex.org/I24603500"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5140839677","display_name":"Faegheh Hasibi","orcid":"https://orcid.org/0009-0006-9986-482X"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Faegheh Hasibi","raw_affiliation_strings":["Radboud University, Nijmegen, Netherlands"],"raw_orcid":"https://orcid.org/0009-0006-9986-482X","affiliations":[{"raw_affiliation_string":"Radboud University, Nijmegen, Netherlands","institution_ids":["https://openalex.org/I145872427"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"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":"1665","last_page":"1676"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":null,"topics":[],"keywords":[{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.48890000581741333},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.2833999991416931},{"id":"https://openalex.org/keywords/uncertainty-analysis","display_name":"Uncertainty analysis","score":0.28279998898506165},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2816999852657318},{"id":"https://openalex.org/keywords/imprecise-probability","display_name":"Imprecise probability","score":0.27230000495910645}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5234000086784363},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.48890000581741333},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42329999804496765},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3107999861240387},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.2833999991416931},{"id":"https://openalex.org/C177803969","wikidata":"https://www.wikidata.org/wiki/Q29205","display_name":"Uncertainty analysis","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2824000120162964},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C130648207","wikidata":"https://www.wikidata.org/wiki/Q6007562","display_name":"Imprecise probability","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2646999955177307},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3805712.3809717","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805712.3809717","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3805712.3809717","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3805712.3809717","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 49th International ACM SIGIR Conference on Research and Development in Information Retrieval","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W3099700870","https://openalex.org/W3183048323","https://openalex.org/W4385570777","https://openalex.org/W4385571189","https://openalex.org/W4385571271","https://openalex.org/W4389518671","https://openalex.org/W4389519118","https://openalex.org/W4389520103","https://openalex.org/W4391376033","https://openalex.org/W4392487838","https://openalex.org/W4398794856","https://openalex.org/W4400526126","https://openalex.org/W4402670143","https://openalex.org/W4402671152","https://openalex.org/W4402671287","https://openalex.org/W4402671618","https://openalex.org/W4402671653","https://openalex.org/W4402671800","https://openalex.org/W4402671856","https://openalex.org/W4412377109","https://openalex.org/W4412377801","https://openalex.org/W4412377895","https://openalex.org/W4412875472","https://openalex.org/W4412888111","https://openalex.org/W4412888299","https://openalex.org/W4412889751","https://openalex.org/W4412944578","https://openalex.org/W4412944909","https://openalex.org/W4412945135","https://openalex.org/W4412945552","https://openalex.org/W4412945694","https://openalex.org/W4415201218","https://openalex.org/W4415798574","https://openalex.org/W4417356729"],"related_works":[],"abstract_inverted_index":{"Retrieval-augmented":[0],"reasoning":[1,14,114,121],"(RAR)":[2],"is":[3,109,239],"a":[4,98],"recent":[5],"evolution":[6],"of":[7,43,66,75,107],"retrieval-augmented":[8],"generation":[9],"(RAG)":[10],"that":[11,177],"employs":[12],"multiple":[13],"steps":[15],"for":[16,22,68,72,89,102,204],"retrieval":[17,55,81],"and":[18,31,82,92,133,150,219,233],"generation.":[19,83],"While":[20],"effective":[21],"some":[23],"complex":[24],"queries,":[25],"RAR":[26,62,69,170],"remains":[27],"vulnerable":[28],"to":[29,39,110,120,159,187],"errors":[30],"misleading":[32],"outputs.":[33,45],"Uncertainty":[34],"quantification":[35],"(UQ)":[36],"offers":[37],"methods":[38],"estimate":[40],"the":[41,112,126,136,140,148,188,209],"confidence":[42],"systems'":[44],"These":[46,123],"methods,":[47],"however,":[48],"often":[49],"handle":[50],"simple":[51],"queries":[52],"with":[53],"no":[54],"or":[56],"single-step":[57],"retrieval,":[58],"without":[59],"properly":[60],"handling":[61],"setup.":[63],"Accurate":[64],"estimation":[65],"UQ":[67,100,190],"requires":[70],"accounting":[71],"all":[73],"sources":[74,91],"uncertainty,":[76],"including":[77],"those":[78],"arising":[79,162],"from":[80,163],"In":[84],"this":[85,144],"paper,":[86],"we":[87],"account":[88],"these":[90],"introduce":[93],"Retrieval-Augmented":[94],"Reasoning":[95],"Consistency":[96],"(R2C),":[97],"novel":[99],"method":[101],"RAR.":[103],"The":[104],"core":[105],"idea":[106],"R2C":[108,178,195],"perturb":[111],"multi-step":[113],"process":[115],"by":[116,181,228],"applying":[117],"various":[118],"actions":[119],"steps.":[122],"perturbations":[124],"alter":[125],"retriever's":[127],"input,":[128],"which":[129],"shifts":[130],"its":[131,202],"output":[132],"consequently":[134],"modifies":[135],"generator's":[137],"input":[138],"at":[139],"next":[141],"step.":[142],"Through":[143],"iterative":[145],"feedback":[146],"loop,":[147],"retriever":[149],"generator":[151],"continuously":[152],"reshape":[153],"each":[154],"other's":[155],"inputs,":[156],"enabling":[157],"us":[158],"capture":[160],"uncertainty":[161],"both":[164,217],"components.":[165],"Experiments":[166],"on":[167,184,241],"five":[168],"popular":[169],"systems":[171],"across":[172],"diverse":[173],"QA":[174],"datasets":[175],"show":[176],"improves":[179,225],"AUROC":[180],"over":[182,230,235],"5%":[183],"average":[185],"compared":[186],"state-of-the-art":[189],"baselines.":[191],"Extrinsic":[192],"evaluations":[193],"using":[194],"as":[196],"an":[197],"external":[198],"signal":[199],"further":[200],"confirm":[201],"effectiveness":[203],"two":[205],"downstream":[206],"tasks:":[207],"in":[208,216,221],"Abstention":[210],"task,":[211],"it":[212,224],"achieves":[213],"~5%":[214],"gains":[215],"F1Abstain":[218],"AccAbstain;":[220],"Model":[222],"Selection,":[223],"exact":[226],"match":[227],"~7%":[229],"single":[231],"models":[232],"~3%":[234],"selection":[236],"methods.":[237],"Code":[238],"available":[240],"https://github.com/HeydarSoudani/R2C.":[242]},"counts_by_year":[],"updated_date":"2026-07-17T05:52:16.776730","created_date":"2026-07-16T00:00:00"}
