{"id":"https://openalex.org/W7160861893","doi":"https://doi.org/10.48550/arxiv.2605.08057","title":"CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation","display_name":"CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160861893","doi":"https://doi.org/10.48550/arxiv.2605.08057"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.08057","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08057","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":"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.2605.08057","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135888678","display_name":"James Petullo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Petullo, James","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5036715761","display_name":"Nianwen Xue","orcid":"https://orcid.org/0000-0002-4364-3618"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Nianwen","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/T10181","display_name":"Natural Language Processing Techniques","score":0.15330000221729279,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.15330000221729279,"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/T10317","display_name":"Advanced Database Systems and Queries","score":0.09600000083446503,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.08500000089406967,"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/leverage","display_name":"Leverage (statistics)","score":0.6484000086784363},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5638999938964844},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5478000044822693},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47769999504089355},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.41670000553131104},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.3456000089645386},{"id":"https://openalex.org/keywords/time-budget","display_name":"Time budget","score":0.34470000863075256},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.3382999897003174}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7540000081062317},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6484000086784363},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5638999938964844},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5478000044822693},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5324000120162964},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.522599995136261},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47769999504089355},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.41670000553131104},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C2986053828","wikidata":"https://www.wikidata.org/wiki/Q355217","display_name":"Time budget","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.31929999589920044},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.3107999861240387},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28189998865127563},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.26980000734329224},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2563000023365021},{"id":"https://openalex.org/C2776904630","wikidata":"https://www.wikidata.org/wiki/Q356336","display_name":"Adept","level":3,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.08057","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08057","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":"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.2605.08057","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.08057","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":"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":{"While":[0],"recent":[1],"advancements":[2],"in":[3,25],"inference-time":[4],"learning":[5,154],"have":[6],"improved":[7],"LLM":[8,110],"reasoning":[9],"on":[10,20,97,139,176],"Text-to-SQL":[11,65],"tasks,":[12],"current":[13],"solutions":[14],"still":[15],"struggle":[16],"to":[17,33,41,51,75,102,116],"perform":[18],"well":[19],"the":[21,26,53,69,78,81,108,118,123,126,140,177],"most":[22],"challenging":[23],"tasks":[24],"Bird-Bench":[27],"(BIRD)":[28],"benchmark.":[29],"This":[30],"is":[31,39],"due":[32],"inadequate":[34],"solution":[35,85,121,132],"space":[36],"exploration,":[37],"which":[38],"necessary":[40],"uncover":[42],"promising":[43],"candidate":[44,120],"queries":[45],"that":[46,67,130,158],"can":[47],"be":[48],"further":[49,103],"refined":[50],"produce":[52],"correct":[54],"output.":[55],"To":[56],"address":[57],"this":[58],"challenge,":[59],"we":[60,89],"introduce":[61],"CA-SQL,":[62],"a":[63,73,91,112,134,166],"novel":[64,113],"pipeline":[66],"utilizes":[68],"estimated":[70],"difficulty":[71],"of":[72,80,99,125,137,143],"task":[74],"dynamically":[76],"scale":[77],"breadth":[79],"exploration":[82],"for":[83],"generating":[84],"candidates.":[86],"In":[87],"addition,":[88],"use":[90],"custom":[92],"prompt":[93],"seeding":[94],"method,":[95],"based":[96],"principles":[98],"evolutionary":[100],"search,":[101],"elicit":[104],"exploratory":[105],"behavior":[106],"from":[107],"base":[109],"and":[111,171],"voting":[114],"method":[115,164],"select":[117],"best":[119],"at":[122],"end":[124],"search.":[127],"Experiments":[128],"demonstrate":[129],"our":[131,163],"achieves":[133],"state-of-the-art":[135],"score":[136,175],"51.72%":[138],"\"challenging\"":[141],"tier":[142],"BIRD":[144,178],"development":[145,179],"set":[146],"problems,":[147],"using":[148],"only":[149],"GPT-4o-mini,":[150],"out-performing":[151],"other":[152],"in-context":[153],"approaches,":[155],"even":[156],"those":[157],"leverage":[159],"larger":[160],"models.":[161],"Overall,":[162],"attains":[165],"competitive":[167],"61.06%":[168],"execution":[169],"accuracy":[170],"68.77%":[172],"Soft":[173],"F1":[174],"dataset.":[180]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-12T00:00:00"}