{"id":"https://openalex.org/W7165747919","doi":"https://doi.org/10.48550/arxiv.2606.24679","title":"FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction","display_name":"FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7165747919","doi":"https://doi.org/10.48550/arxiv.2606.24679"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.24679","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24679","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":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.2606.24679","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125703032","display_name":"Kunyu Ni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ni, Kunyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139248342","display_name":"Lei Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139279136","display_name":"Jie He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139240653","display_name":"Xiaotong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiaotong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139221941","display_name":"Jianfeng Jin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jin, Jianfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139252665","display_name":"Junyu Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Junyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139229771","display_name":"Yanwei Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Yanwei","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/T13702","display_name":"Machine Learning in Healthcare","score":0.48080000281333923,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.48080000281333923,"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/T11719","display_name":"Data Quality and Management","score":0.1420000046491623,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.052400000393390656,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/pipeline","display_name":"Pipeline (software)","score":0.6744999885559082},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5616000294685364},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5533999800682068},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5033000111579895},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4991999864578247},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4551999866962433},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.4113999903202057},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4068000018596649},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.3693000078201294}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7329999804496765},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6744999885559082},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5616000294685364},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5533999800682068},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5220999717712402},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5033000111579895},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4991999864578247},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45840001106262207},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4551999866962433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44850000739097595},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.4113999903202057},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4068000018596649},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.3693000078201294},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3659000098705292},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.3508000075817108},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.34369999170303345},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.3407000005245209},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3246999979019165},{"id":"https://openalex.org/C489000","wikidata":"https://www.wikidata.org/wiki/Q747385","display_name":"Data flow diagram","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C92446256","wikidata":"https://www.wikidata.org/wiki/Q3306762","display_name":"Data validation","level":2,"score":0.28119999170303345},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2784999907016754},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2750999927520752},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.2574999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.24679","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24679","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":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.2606.24679","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24679","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.510128915309906}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Data":[0],"preparation":[1],"pipelines":[2,27],"improve":[3],"data":[4,15],"quality":[5],"in":[6,71],"machine":[7],"learning":[8],"by":[9,187],"transforming":[10],"raw":[11],"tables":[12],"into":[13,64,156],"learning-ready":[14],"through":[16,131],"sequential":[17],"cleaning":[18],"and":[19,36,67,164,191],"feature":[20],"transformation":[21],"operators.":[22],"However,":[23],"automatically":[24],"constructing":[25],"such":[26],"is":[28,39,60,69,199],"computationally":[29],"difficult":[30],"because":[31],"operator":[32],"sequences":[33],"are":[34],"combinatorial":[35],"end-to-end":[37],"evaluation":[38],"expensive.":[40],"Existing":[41],"state-of-the-art":[42],"(SOTA)":[43],"Multi-DQN":[44],"methods":[45],"still":[46],"face":[47],"three":[48],"key":[49],"limitations:":[50],"decoupled":[51],"value":[52],"estimators":[53],"weaken":[54],"long-horizon":[55],"credit":[56],"assignment,":[57],"dataset":[58,148],"context":[59],"only":[61],"weakly":[62],"injected":[63],"the":[65,142,157],"policy,":[66],"exploration":[68],"inefficient":[70],"a":[72,87,100,112],"sparse":[73],"search":[74,166],"space":[75],"with":[76,111,121,175],"many":[77],"invalid":[78,162],"states.":[79],"To":[80],"address":[81],"these":[82],"issues,":[83],"we":[84],"propose":[85],"FlowPipe,":[86],"unified":[88],"framework":[89],"that":[90,180],"formulates":[91],"pipeline":[92,123],"synthesis":[93],"as":[94],"conditional":[95],"probabilistic":[96],"flow":[97,158],"generation":[98],"over":[99],"directed":[101],"acyclic":[102],"graph.":[103],"FlowPipe":[104,152,181],"uses":[105],"Conditional":[106],"Generative":[107],"Flow":[108],"Networks":[109],"(C-GFlowNets)":[110],"Trajectory":[113],"Balance":[114],"objective":[115,159],"to":[116,140,147,160],"connect":[117],"terminal":[118],"validation":[119],"rewards":[120],"early":[122],"decisions.":[124],"It":[125],"further":[126],"introduces":[127],"Deep":[128],"Semantic":[129],"Modulation":[130,134],"Feature-wise":[132],"Linear":[133],"(FiLM),":[135],"allowing":[136],"LLM-derived":[137],"logical":[138],"priors":[139],"condition":[141],"policy's":[143],"internal":[144],"activations":[145],"according":[146],"semantics.":[149],"In":[150],"addition,":[151],"incorporates":[153],"failure":[154],"awareness":[155],"avoid":[161],"states":[163],"concentrate":[165],"on":[167,171,189],"high-potential":[168],"regions.":[169],"Experiments":[170],"two":[172],"benchmark":[173],"suites":[174],"74":[176],"real-world":[177],"datasets":[178],"show":[179],"outperforms":[182],"SOTA":[183],"baselines,":[184],"improving":[185],"accuracy":[186],"11.96%":[188],"average":[190],"achieving":[192],"12.5x":[193],"faster":[194],"training":[195],"convergence.":[196],"Source":[197],"code":[198],"available":[200],"at":[201],"https://github.com/KunyuNi/FlowPipe.":[202]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-25T00:00:00"}