{"id":"https://openalex.org/W7139954810","doi":"https://doi.org/10.48550/arxiv.2603.18018","title":"An Agentic System for Schema Aware NL2SQL Generation","display_name":"An Agentic System for Schema Aware NL2SQL Generation","publication_year":2026,"publication_date":"2026-02-25","ids":{"openalex":"https://openalex.org/W7139954810","doi":"https://doi.org/10.48550/arxiv.2603.18018"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.18018","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18018","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.2603.18018","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130214668","display_name":"David Onyango","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Onyango, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5004003516","display_name":"Naseef Mansoor","orcid":"https://orcid.org/0000-0003-4280-697X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mansoor, Naseef","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/T10101","display_name":"Cloud Computing and Resource Management","score":0.2863999903202057,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.2863999903202057,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.19589999318122864,"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/T14347","display_name":"Big Data and Digital Economy","score":0.12919999659061432,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/schema","display_name":"Schema (genetic algorithms)","score":0.6484000086784363},{"id":"https://openalex.org/keywords/sql","display_name":"SQL","score":0.5817999839782715},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.49939998984336853},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.4278999865055084},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.4244999885559082},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3797000050544739},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3587999939918518},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3427000045776367}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8603000044822693},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.6484000086784363},{"id":"https://openalex.org/C510870499","wikidata":"https://www.wikidata.org/wiki/Q47607","display_name":"SQL","level":2,"score":0.5817999839782715},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.49939998984336853},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.4278999865055084},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.4244999885559082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39800000190734863},{"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/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3427000045776367},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.32760000228881836},{"id":"https://openalex.org/C30775581","wikidata":"https://www.wikidata.org/wiki/Q632285","display_name":"Database schema","level":3,"score":0.32749998569488525},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.32670000195503235},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.3264000117778778},{"id":"https://openalex.org/C56288433","wikidata":"https://www.wikidata.org/wiki/Q58673","display_name":"Data manipulation language","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3206000030040741},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.31200000643730164},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28220000863075256},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.25999999046325684}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.18018","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18018","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.2603.18018","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.18018","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0,86,147],"natural":[1],"language":[2],"to":[3,19,134,158],"SQL":[4],"(NL2SQL)":[5],"task":[6,35],"plays":[7],"a":[8,64,81,122],"pivotal":[9],"role":[10],"in":[11,55,95],"democratizing":[12],"data":[13,50],"access":[14],"by":[15,80],"enabling":[16],"non-expert":[17],"users":[18],"interact":[20],"with":[21],"relational":[22],"databases":[23],"through":[24],"intuitive":[25],"language.":[26],"While":[27],"recent":[28],"frameworks":[29],"have":[30],"enhanced":[31],"translation":[32],"accuracy":[33,118],"via":[34],"specialization,":[36],"their":[37],"reliance":[38],"on":[39,107],"Large":[40],"Language":[41,73],"Models":[42,74],"(LLMs)":[43],"raises":[44],"significant":[45],"concerns":[46],"regarding":[47],"computational":[48,103],"overhead,":[49],"privacy,":[51],"and":[52,121],"real-world":[53],"deployability":[54],"resource-constrained":[56],"environments.":[57],"To":[58],"address":[59],"these":[60],"challenges,":[61],"we":[62],"propose":[63],"schema":[65],"based":[66],"agentic":[67],"system":[68,100,114,148],"that":[69,112],"strategically":[70],"employs":[71],"Small":[72],"(SLMs)":[75],"as":[76,137],"primary":[77],"agents,":[78],"complemented":[79],"selective":[82],"LLM":[83,87],"fallback":[84],"mechanism.":[85],"is":[88],"invoked":[89],"only":[90],"upon":[91],"detection":[92],"of":[93,119,126,140,155],"errors":[94],"SLM-generated":[96],"output,":[97],"the":[98,108],"proposed":[99],"significantly":[101],"minimizes":[102],"expenditure.":[104],"Experimental":[105],"results":[106],"BIRD":[109],"benchmark":[110],"demonstrate":[111],"our":[113],"achieves":[115,149],"an":[116,150],"execution":[117],"47.78%":[120],"validation":[123],"efficiency":[124],"score":[125],"51.05%,":[127],"achieving":[128,163],"over":[129],"90%":[130],"cost":[131,152],"reduction":[132],"compared":[133,157],"LLM-centric":[135],"baselines":[136],"approximately":[138],"67%":[139],"queries":[141],"are":[142],"resolved":[143],"using":[144],"local":[145],"SLMs.":[146],"average":[151],"per":[153],"query":[154],"0.0085":[156],"0.094":[159],"for":[160,167],"LLM-only":[161],"systems,":[162],"near-zero":[164],"operational":[165],"costs":[166],"locally":[168],"executed":[169],"queries.":[170],"[Github":[171],"repository:":[172],"https://github.com/mindslab25/CESMA.]":[173]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-21T00:00:00"}
