{"id":"https://openalex.org/W7160622562","doi":"https://doi.org/10.48550/arxiv.2605.06647","title":"Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval","display_name":"Superintelligent Retrieval Agent: The Next Frontier of Agentic Retrieval","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160622562","doi":"https://doi.org/10.48550/arxiv.2605.06647"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06647","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,"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.2605.06647","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135655741","display_name":"Zeyu Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Zeyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135646396","display_name":"Qi Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Qi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135666113","display_name":"Jason Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jason","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135660483","display_name":"Anshumali Shrivastava","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shrivastava, Anshumali","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":0.2614000141620636,"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":0.2614000141620636,"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/T13274","display_name":"Expert finding and Q&A systems","score":0.07450000196695328,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.04470000043511391,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/document-retrieval","display_name":"Document retrieval","score":0.666700005531311},{"id":"https://openalex.org/keywords/terminology","display_name":"Terminology","score":0.5735999941825867},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5569999814033508},{"id":"https://openalex.org/keywords/query-expansion","display_name":"Query expansion","score":0.550000011920929},{"id":"https://openalex.org/keywords/human\u2013computer-information-retrieval","display_name":"Human\u2013computer information retrieval","score":0.5044000148773193},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.45829999446868896},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.4239000082015991},{"id":"https://openalex.org/keywords/concept-search","display_name":"Concept search","score":0.400299996137619},{"id":"https://openalex.org/keywords/cognitive-models-of-information-retrieval","display_name":"Cognitive models of information retrieval","score":0.3644999861717224}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7595999836921692},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.7552000284194946},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.666700005531311},{"id":"https://openalex.org/C547195049","wikidata":"https://www.wikidata.org/wiki/Q1725664","display_name":"Terminology","level":2,"score":0.5735999941825867},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5569999814033508},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.550000011920929},{"id":"https://openalex.org/C90288658","wikidata":"https://www.wikidata.org/wiki/Q3318149","display_name":"Human\u2013computer information retrieval","level":3,"score":0.5044000148773193},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.45829999446868896},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.4239000082015991},{"id":"https://openalex.org/C182861755","wikidata":"https://www.wikidata.org/wiki/Q5158391","display_name":"Concept search","level":4,"score":0.400299996137619},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38760000467300415},{"id":"https://openalex.org/C21025794","wikidata":"https://www.wikidata.org/wiki/Q5141219","display_name":"Cognitive models of information retrieval","level":4,"score":0.3644999861717224},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.36329999566078186},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C191072391","wikidata":"https://www.wikidata.org/wiki/Q17043235","display_name":"Retrievability","level":3,"score":0.3452000021934509},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.31709998846054077},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C2779532271","wikidata":"https://www.wikidata.org/wiki/Q445558","display_name":"Relevance feedback","level":4,"score":0.2752000093460083},{"id":"https://openalex.org/C108757681","wikidata":"https://www.wikidata.org/wiki/Q2773912","display_name":"Bigram","level":3,"score":0.2703000009059906},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.2651999890804291},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C551230270","wikidata":"https://www.wikidata.org/wiki/Q4368942","display_name":"Data retrieval","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06647","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,"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.2605.06647","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06647","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,"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":"Reduced inequalities","score":0.7242377400398254,"id":"https://metadata.un.org/sdg/10"}],"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],"agents":[1,238],"are":[2,90,134],"increasingly":[3],"the":[4,97,120,154,157,166],"interface":[5],"to":[6,140],"large":[7],"knowledge":[8],"bases,":[9],"yet":[10],"most":[11],"treat":[12],"retrieval":[13,55,71,81,142,169],"as":[14,72,127],"a":[15,32,78,148,213,221],"black":[16],"box:":[17],"they":[18],"issue":[19],"exploratory":[20,75],"queries,":[21],"inspect":[22],"snippets,":[23],"and":[24,50,58,123,180,207,247],"reformulate":[25],"until":[26],"evidence":[27,99,118],"emerges.":[28],"This":[29],"resembles":[30],"how":[31,40],"newcomer":[33],"searches":[34],"an":[35,41,104],"unfamiliar":[36],"database":[37],"rather":[38],"than":[39],"expert":[42],"navigates":[43],"it":[44,92,116],"with":[45,109,156],"strong":[46],"priors":[47],"about":[48],"terminology":[49],"likely":[51],"evidence,":[52],"causing":[53],"extra":[54],"rounds,":[56],"latency,":[57],"poor":[59],"recall.":[60],"We":[61,209],"introduce":[62,211],"\\textit{Superintelligent":[63],"Retrieval":[64],"Agent}":[65],"(SIRA),":[66],"which":[67,88,94],"casts":[68],"\\emph{superintelligence}":[69],"in":[70,171],"compressing":[73],"multi-round":[74,236],"search":[76,111],"into":[77],"single":[79,149],"corpus-discriminative":[80],"action.":[82],"SIRA":[83,164,234],"does":[84],"not":[85],"merely":[86],"ask":[87],"terms":[89,95,132],"relevant;":[91],"asks":[93],"separate":[96],"desired":[98],"from":[100],"corpus-level":[101],"confusers.":[102],"Offline,":[103],"LLM":[105,181],"enriches":[106],"each":[107],"document":[108],"missing":[110],"vocabulary;":[112],"at":[113,239],"query":[114,121,155],"time,":[115],"predicts":[117],"vocabulary":[119],"omits;":[122],"corpus":[124],"statistics":[125],"serve":[126],"tool":[128],"calls":[129],"that":[130,133],"filter":[131],"absent,":[135],"overly":[136],"common,":[137],"or":[138,189],"unlikely":[139],"create":[141],"margin.":[143],"The":[144],"final":[145],"step":[146],"is":[147],"weighted":[150],"BM25":[151],"call":[152],"combining":[153],"validated":[158],"expansion.":[159],"Across":[160],"ten":[161],"BEIR":[162],"benchmarks,":[163],"achieves":[165],"strongest":[167],"average":[168],"performance":[170],"our":[172],"comparison,":[173],"beating":[174],"dense":[175],"retrievers,":[176,179],"learned":[177],"sparse":[178],"search-agent":[182],"baselines":[183],"while":[184],"using":[185,229],"no":[186],"relevance":[187],"labels":[188],"retriever":[190],"fine-tuning.":[191],"On":[192],"downstream":[193],"QA,":[194],"its":[195],"retrieval-only":[196],"answer":[197],"coverage":[198],"exceeds":[199],"recent":[200],"RL-trained":[201],"agentic":[202],"QA":[203],"systems":[204],"on":[205],"NQ":[206],"HotpotQA.":[208],"also":[210],"\\textbf{BrowseComp-Wikipedia},":[212],"hard-search":[214],"benchmark":[215],"of":[216],"232":[217],"BrowseComp-derived":[218],"queries":[219],"over":[220],"25,587,229-document":[222],"Wikipedia":[223,232],"index.":[224],"Even":[225],"without":[226],"index-time":[227],"enrichment,":[228],"only":[230],"grounded":[231],"categories,":[233],"outperforms":[235],"Perplexity":[237],"every":[240],"budget,":[241],"reaching":[242],"9.70%":[243],"Recall@1,":[244],"15.27%":[245],"Recall@10,":[246],"36.14%":[248],"Recall@100.":[249]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-09T00:00:00"}
