{"id":"https://openalex.org/W7163564115","doi":"https://doi.org/10.48550/arxiv.2606.04320","title":"OpenRFM: Dissecting Relational In-Context Learning","display_name":"OpenRFM: Dissecting Relational In-Context Learning","publication_year":2026,"publication_date":"2026-06-03","ids":{"openalex":"https://openalex.org/W7163564115","doi":"https://doi.org/10.48550/arxiv.2606.04320"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.04320","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04320","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.2606.04320","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137879912","display_name":"Zhikai Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Zhikai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137860317","display_name":"Junyu Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Junyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137855138","display_name":"Jialiang Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Jialiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037205304","display_name":"Siheng Xiong","orcid":"https://orcid.org/0000-0002-5274-9457"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Siheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063552937","display_name":"Xiaoze Liu","orcid":"https://orcid.org/0000-0002-9726-3397"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xiaoze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126300871","display_name":"Ruowang Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ruowang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063326523","display_name":"Keren Zhou","orcid":"https://orcid.org/0000-0002-7977-3182"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Keren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137909842","display_name":"Kai Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Kai","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":0.1363999992609024,"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":0.1363999992609024,"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":0.13279999792575836,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.07000000029802322,"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/relational-database","display_name":"Relational database","score":0.6905999779701233},{"id":"https://openalex.org/keywords/statistical-relational-learning","display_name":"Statistical relational learning","score":0.6478000283241272},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.520799994468689},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.491100013256073},{"id":"https://openalex.org/keywords/relational-model","display_name":"Relational model","score":0.4713999927043915},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.447299987077713},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.40720000863075256},{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.3587000072002411},{"id":"https://openalex.org/keywords/underdetermined-system","display_name":"Underdetermined system","score":0.35580000281333923}],"concepts":[{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.6905999779701233},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6611999869346619},{"id":"https://openalex.org/C177877439","wikidata":"https://www.wikidata.org/wiki/Q7604413","display_name":"Statistical relational learning","level":3,"score":0.6478000283241272},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5436000227928162},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.520799994468689},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.491100013256073},{"id":"https://openalex.org/C40207289","wikidata":"https://www.wikidata.org/wiki/Q755662","display_name":"Relational model","level":3,"score":0.4713999927043915},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.447299987077713},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4219000041484833},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40720000863075256},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3630000054836273},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C179690561","wikidata":"https://www.wikidata.org/wiki/Q4316110","display_name":"Underdetermined system","level":2,"score":0.35580000281333923},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C65647387","wikidata":"https://www.wikidata.org/wiki/Q1781706","display_name":"Conjunctive query","level":3,"score":0.3479999899864197},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3443000018596649},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3310999870300293},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.32910001277923584},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C103593891","wikidata":"https://www.wikidata.org/wiki/Q624546","display_name":"Entity\u2013relationship model","level":3,"score":0.30160000920295715},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.2989000082015991},{"id":"https://openalex.org/C95916125","wikidata":"https://www.wikidata.org/wiki/Q840540","display_name":"Relational algebra","level":3,"score":0.29339998960494995},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.28299999237060547},{"id":"https://openalex.org/C99436015","wikidata":"https://www.wikidata.org/wiki/Q1722436","display_name":"Relational calculus","level":4,"score":0.27959999442100525},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2791999876499176},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.26080000400543213},{"id":"https://openalex.org/C557471498","wikidata":"https://www.wikidata.org/wiki/Q554950","display_name":"Recommender system","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.04320","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04320","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.2606.04320","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.04320","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Relational":[0,53],"Foundation":[1],"Models":[2],"(RFMs)":[3],"promise":[4],"a":[5,26,49,69,119,133,143,149,161,171,178,202],"single":[6],"pre-trained":[7,150],"predictor":[8],"that,":[9],"given":[10],"any":[11],"relational":[12,21,121,140],"database,":[13],"returns":[14],"predictions":[15],"in":[16,123],"one":[17],"forward":[18],"pass":[19],"via":[20],"in-context":[22],"learning":[23],"(ICL).":[24],"Yet":[25],"substantial":[27],"gap":[28,41,112],"separates":[29],"open":[30],"RFMs":[31],"from":[32,56,148],"their":[33],"commercial":[34,198],"counterparts,":[35],"and":[36,68,91,97,159,195],"the":[37,52,101,115,124,139,192,197],"origin":[38],"of":[39,205],"this":[40,111],"has":[42],"not":[43],"been":[44],"systematically":[45],"understood.":[46],"We":[47],"dissect":[48],"representative":[50],"framework,":[51],"Transformer":[54],"(RT),":[55],"two":[57,128],"perspectives.":[58],"Model":[59],"side:":[60,85],"we":[61,86],"show":[62],"that":[63,93,114,137,183],"RT":[64,193],"performs":[65],"relation-level":[66,156],"ICL,":[67],"kernel":[70],"regression":[71],"view":[72],"shows":[73],"it":[74],"fails":[75],"when":[76],"sparse":[77],"label-cell":[78],"coverage":[79],"yields":[80],"an":[81],"underdetermined":[82],"regression.":[83],"Data":[84],"ablate":[87],"RT's":[88],"pre-training":[89,96,99,167],"source":[90],"find":[92],"existing":[94],"synthetic-only":[95],"in-distribution":[98],"drive":[100],"same":[102],"architecture":[103,136],"into":[104,131],"different":[105],"regimes,":[106],"lazy":[107],"vs.":[108],"feature-learning.":[109],"Probing":[110],"reveals":[113],"missing":[116],"ingredient":[117],"is":[118],"support-identifiable":[120],"latent":[122],"label-generation":[125],"process.":[126],"These":[127,174],"diagnoses":[129],"translate":[130],"(1)":[132],"dual-stage":[134],"ICL":[135,145],"combines":[138],"backbone":[141,194],"with":[142,170],"batch-level":[144],"layer":[146],"lifted":[147],"tabular":[151],"foundation":[152],"model":[153,199],"to":[154],"overcome":[155],"label":[157],"scarcity,":[158],"(2)":[160],"homophily-aware":[162],"synthetic":[163],"plus":[164],"continual":[165],"real-data":[166],"mixture,":[168],"augmented":[169],"prototype-based":[172],"regularization.":[173],"choices":[175],"define":[176],"OpenRFM,":[177],"simple":[179],"yet":[180],"effective":[181],"RFM":[182],"improves":[184],"average":[185],"task":[186],"performance":[187],"by":[188],"approximately":[189],"30%":[190],"over":[191],"surpasses":[196],"KumoRFMv1":[200],"on":[201],"large":[203],"set":[204],"evaluation":[206],"tasks.":[207]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-05T00:00:00"}
