{"id":"https://openalex.org/W7140089314","doi":"https://doi.org/10.48550/arxiv.2603.20004","title":"ReViSQL: Achieving Human-Level Text-to-SQL","display_name":"ReViSQL: Achieving Human-Level Text-to-SQL","publication_year":2026,"publication_date":"2026-03-20","ids":{"openalex":"https://openalex.org/W7140089314","doi":"https://doi.org/10.48550/arxiv.2603.20004"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.20004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20004","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":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.2603.20004","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130335611","display_name":"Yuxuan Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yuxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103188343","display_name":"Tengjun Jin","orcid":"https://orcid.org/0009-0005-0353-4184"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Tengjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101833423","display_name":"Y. M. Choi","orcid":"https://orcid.org/0009-0005-1413-1401"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Yoojin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130403710","display_name":"Daniel Kang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Daniel","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/T12535","display_name":"Machine Learning and Data Classification","score":0.09860000014305115,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.09860000014305115,"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/T11986","display_name":"Scientific Computing and Data Management","score":0.08640000224113464,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.07039999961853027,"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":0.6152999997138977},{"id":"https://openalex.org/keywords/sql","display_name":"SQL","score":0.5490000247955322},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3797000050544739},{"id":"https://openalex.org/keywords/closing","display_name":"Closing (real estate)","score":0.3716999888420105},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.35600000619888306},{"id":"https://openalex.org/keywords/verifiable-secret-sharing","display_name":"Verifiable secret sharing","score":0.35269999504089355},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.3504999876022339},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.3495999872684479},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3488999903202057}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8287000060081482},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.6152999997138977},{"id":"https://openalex.org/C510870499","wikidata":"https://www.wikidata.org/wiki/Q47607","display_name":"SQL","level":2,"score":0.5490000247955322},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4618000090122223},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39469999074935913},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C2778775528","wikidata":"https://www.wikidata.org/wiki/Q5135432","display_name":"Closing (real estate)","level":2,"score":0.3716999888420105},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.35600000619888306},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.35269999504089355},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.34529998898506165},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3264999985694885},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.319599986076355},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.3050999939441681},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C56288433","wikidata":"https://www.wikidata.org/wiki/Q58673","display_name":"Data manipulation language","level":2,"score":0.2847000062465668},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2815000116825104},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.27900001406669617},{"id":"https://openalex.org/C154420247","wikidata":"https://www.wikidata.org/wiki/Q846619","display_name":"Stored procedure","level":5,"score":0.2757999897003174},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.27129998803138733},{"id":"https://openalex.org/C33762810","wikidata":"https://www.wikidata.org/wiki/Q461671","display_name":"Data integrity","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.20004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20004","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":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.2603.20004","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.20004","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":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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Translating":[0],"natural":[1],"language":[2,29],"to":[3,90],"SQL":[4,24,92,156],"(Text-to-SQL)":[5],"is":[6],"a":[7,50,101,129,149,167,257],"critical":[8],"challenge":[9],"in":[10,164],"both":[11],"database":[12],"research":[13],"and":[14,31,152,160,205,221,240],"data":[15,89,150,162,180],"analytics":[16],"applications.":[17],"Recent":[18],"efforts":[19],"have":[20,59],"focused":[21],"on":[22,66,108,127,139,174],"enhancing":[23],"reasoning":[25,93],"by":[26,187,247],"developing":[27],"large":[28],"models":[30],"AI":[32,57,117],"agents":[33,58],"that":[34,75,104,178],"decompose":[35],"Text-to-SQL":[36,136],"tasks":[37],"into":[38],"manually":[39],"designed,":[40],"step-by-step":[41],"pipelines.":[42],"However,":[43],"despite":[44],"these":[45],"extensive":[46],"architectural":[47,83],"engineering":[48],"efforts,":[49],"significant":[51],"gap":[52,78],"remains:":[53],"even":[54],"state-of-the-art":[55],"(SOTA)":[56],"not":[60,80],"yet":[61],"achieved":[62],"the":[63,67,95,111,140,184,190,211,235,242,253],"human-level":[64,106,237],"accuracy":[65,107,186,238],"BIRD":[68,109,141,170,226],"benchmark.":[69],"In":[70],"this":[71,77],"paper,":[72],"we":[73,131,147,176,209],"show":[74,177],"closing":[76],"does":[79],"require":[81],"further":[82,195],"complexity,":[84],"but":[85],"rather":[86],"clean":[87],"training":[88,173],"improve":[91],"of":[94,115,166,169,213],"underlying":[96],"models.":[97],"We":[98,158],"introduce":[99],"ReViSQL,":[100],"streamlined":[102],"framework":[103,215],"achieves":[105,230],"for":[110],"first":[112],"time.":[113],"Instead":[114],"complex":[116],"agents,":[118],"ReViSQL":[119,198],"leverages":[120],"reinforcement":[121],"learning":[122],"with":[123,216],"verifiable":[124],"rewards":[125],"(RLVR)":[126],"BIRD-Verified,":[128,146,175],"dataset":[130],"curated":[132],"comprising":[133],"2.5k":[134],"verified":[135],"instances":[137],"based":[138],"Train":[142],"set.":[143],"To":[144,194],"construct":[145],"design":[148],"correction":[151],"verification":[153],"workflow":[154],"involving":[155],"experts.":[157],"identified":[159],"corrected":[161],"errors":[163],"61.1%":[165],"subset":[168],"Train.":[171],"By":[172],"improving":[179],"quality":[181],"alone":[182],"boosts":[183],"single-generation":[185],"8.2-13.9%":[188],"under":[189],"same":[191],"RLVR":[192],"algorithm.":[193],"enhance":[196],"performance,":[197],"performs":[199],"inference-time":[200],"scaling":[201],"via":[202],"execution-based":[203],"reconciliation":[204],"majority":[206],"voting.":[207],"Empirically,":[208],"demonstrate":[210],"superiority":[212],"our":[214],"two":[217],"model":[218],"scales:":[219],"ReViSQL-235B-A22B":[220,229],"ReViSQL-30B-A3B.":[222],"On":[223],"an":[224],"expert-verified":[225],"Mini-Dev":[227],"set,":[228],"93.2%":[231],"execution":[232],"accuracy,":[233],"exceeding":[234],"proxy":[236],"(92.96%)":[239],"outperforming":[241],"prior":[243,254],"open-source":[244],"SOTA":[245,255],"method":[246],"9.8%.":[248],"Our":[249],"lightweight":[250],"ReViSQL-30B-A3B":[251],"matches":[252],"at":[256],"7.5$\\times$":[258],"lower":[259],"per-query":[260],"cost.":[261]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-24T00:00:00"}
