{"id":"https://openalex.org/W7138454023","doi":"https://doi.org/10.48550/arxiv.2603.13308","title":"Task Expansion and Cross Refinement for Open-World Conditional Modeling","display_name":"Task Expansion and Cross Refinement for Open-World Conditional Modeling","publication_year":2026,"publication_date":"2026-03-03","ids":{"openalex":"https://openalex.org/W7138454023","doi":"https://doi.org/10.48550/arxiv.2603.13308"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.13308","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13308","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.13308","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029353174","display_name":"Shreyas Bhat Brahmavar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brahmavar, Shreyas Bhat","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129659292","display_name":"Qiyang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qiyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129748007","display_name":"Yang Li","orcid":"https://orcid.org/0009-0003-0093-4284"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129709105","display_name":"Junier Oliva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oliva, Junier","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.2597000002861023,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.2597000002861023,"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/T10028","display_name":"Topic Modeling","score":0.1826000064611435,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.11800000071525574,"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/task","display_name":"Task (project management)","score":0.6937000155448914},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.6279000043869019},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6277999877929688},{"id":"https://openalex.org/keywords/structured-prediction","display_name":"Structured prediction","score":0.4350999891757965},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3919999897480011},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.37929999828338623},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.3709000051021576}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7469000220298767},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6937000155448914},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.6279000043869019},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6277999877929688},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.4350999891757965},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42170000076293945},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4043999910354614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.398499995470047},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3919999897480011},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3824000060558319},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.37929999828338623},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37529999017715454},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3709000051021576},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C44492722","wikidata":"https://www.wikidata.org/wiki/Q327069","display_name":"Conditional probability","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2985000014305115},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.13308","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13308","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.13308","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13308","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":"article"},"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":{"Open-world":[0],"conditional":[1,11,128,156],"modeling":[2],"(OCM),":[3],"requires":[4],"a":[5,25,39,53,126],"single":[6],"model":[7],"to":[8,107,124],"answer":[9],"arbitrary":[10],"queries":[12],"across":[13,105],"heterogeneous":[14,131],"datasets,":[15],"where":[16],"observed":[17],"variables":[18],"and":[19,22,49,64,77,101,111,139,151],"targets":[20],"vary":[21],"arise":[23],"from":[24],"vast":[26],"open-ended":[27],"task":[28,59,149],"universe.":[29],"Because":[30],"any":[31],"finite":[32],"collection":[33],"of":[34,42,66],"real-world":[35],"datasets":[36,118],"covers":[37],"only":[38],"small":[40],"fraction":[41],"this":[43],"space,":[44],"we":[45],"propose":[46],"Task":[47],"Expansion":[48],"Cross":[50],"Refinement":[51],"(TEXR),":[52],"semi-supervised":[54],"framework":[55],"that":[56,147],"enlarges":[57],"effective":[58],"coverage":[60],"through":[61],"structured":[62,82,148],"synthesis":[63],"refinement":[65,94,153],"semantic":[67],"data":[68,99,123],"contexts.":[69],"TEXR":[70,134],"first":[71],"generates":[72],"diverse":[73],"uninstantiated":[74],"dataset":[75],"schemas":[76],"weakly":[78],"instantiates":[79],"them":[80],"via":[81],"probabilistic":[83],"generators":[84],"guided":[85],"by":[86,95],"large":[87],"language":[88],"models.":[89],"It":[90],"then":[91],"performs":[92],"cross-model":[93],"training":[96],"on":[97],"disjoint":[98],"partitions":[100],"revising":[102],"synthetic":[103,117],"values":[104],"splits":[106],"reduce":[108],"confirmation":[109],"bias":[110],"improve":[112],"pseudo-value":[113],"quality.":[114],"The":[115],"refined":[116],"are":[119],"aggregated":[120],"with":[121],"real":[122],"train":[125],"unified":[127],"model.":[129],"Across":[130],"tabular":[132],"benchmarks,":[133],"consistently":[135],"improves":[136],"zero-,":[137],"few-,":[138],"many-shot":[140],"performance":[141],"for":[142],"multiple":[143],"OCM":[144],"backbones,":[145],"demonstrating":[146],"expansion":[150],"cross":[152],"enhance":[154],"open-world":[155],"modeling.":[157]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-03-18T00:00:00"}