{"id":"https://openalex.org/W7166710320","doi":"https://doi.org/10.48550/arxiv.2606.30410","title":"Beyond IID: How General Are Tabular Foundation Models, Really?","display_name":"Beyond IID: How General Are Tabular Foundation Models, Really?","publication_year":2026,"publication_date":"2026-06-29","ids":{"openalex":"https://openalex.org/W7166710320","doi":"https://doi.org/10.48550/arxiv.2606.30410"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.30410","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30410","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.2606.30410","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139641955","display_name":"Lennart Purucker","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Purucker, Lennart","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078207759","display_name":"Andrej Tschalzev","orcid":"https://orcid.org/0000-0002-0638-5744"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tschalzev, Andrej","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011601027","display_name":"Nick Erickson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Erickson, Nick","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139637489","display_name":"Gioia Blayer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Blayer, Gioia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036807127","display_name":"David Holzm\u00fcller","orcid":"https://orcid.org/0000-0002-9443-0049"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Holzm\u00fcller, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120446747","display_name":"Alan Arazi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arazi, Alan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115617332","display_name":"Alexander Pfefferle","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pfefferle, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139638061","display_name":"Mustafa Tajjar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tajjar, Mustafa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139635549","display_name":"Ga\u00ebl Varoquaux","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Varoquaux, Ga\u00ebl","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139669156","display_name":"Frank Hutter","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hutter, Frank","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/T12535","display_name":"Machine Learning and Data Classification","score":0.31929999589920044,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.31929999589920044,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.06390000134706497,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.666100025177002},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4607999920845032},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.42809998989105225},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.39570000767707825},{"id":"https://openalex.org/keywords/data-type","display_name":"Data type","score":0.373199999332428},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.3700000047683716},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.34599998593330383},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.34040001034736633}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.714900016784668},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.666100025177002},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4607999920845032},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.44029998779296875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43950000405311584},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.42809998989105225},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4162999987602234},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.39570000767707825},{"id":"https://openalex.org/C138958017","wikidata":"https://www.wikidata.org/wiki/Q190087","display_name":"Data type","level":2,"score":0.373199999332428},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C2778473407","wikidata":"https://www.wikidata.org/wiki/Q1459574","display_name":"Compendium","level":2,"score":0.32010000944137573},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31839999556541443},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C52146309","wikidata":"https://www.wikidata.org/wiki/Q7431116","display_name":"Schema (genetic algorithms)","level":2,"score":0.3068000078201294},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C2780767217","wikidata":"https://www.wikidata.org/wiki/Q5532421","display_name":"Generality","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.30410","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30410","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.2606.30410","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30410","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":[{"score":0.6224880814552307,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Foundation":[0],"models":[1,27,71,174,184,198],"for":[2,66,112,162,166,211],"predictive":[3,167],"machine":[4,168],"learning":[5,197],"on":[6,28,59,88,91,96,186,201],"tabular":[7,25,69,113,164,182,217,224],"data":[8,93,114],"have":[9],"recently":[10],"gained":[11],"significant":[12],"traction":[13],"in":[14,83,216],"academia":[15],"and":[16,31,36,48,126,159,175,195,204],"industry.":[17],"Research":[18],"communities":[19],"across":[20,123,172],"disciplines":[21],"are":[22,51,63,78],"increasingly":[23],"evaluating":[24],"foundation":[26,70,183],"diverse":[29,117,131],"datasets":[30,165,178],"tasks.":[32],"However,":[33],"these":[34],"task-":[35],"discipline-specific":[37],"evaluations":[38],"remain":[39],"largely":[40],"inaccessible":[41],"to":[42,188],"model":[43,56,209],"researchers":[44,57],"because":[45],"benchmark":[46,111],"software":[47],"evaluation":[49],"protocols":[50],"fragmented.":[52],"As":[53],"a":[54,140,156],"result,":[55],"rely":[58],"standard":[60,150],"benchmarks,":[61,151],"which":[62],"mostly":[64],"defined":[65],"tasks":[67],"where":[68],"already":[72],"excel.":[73],"The":[74],"most":[75,213],"challenging":[76],"scenarios":[77],"excluded,":[79],"limiting":[80],"meaningful":[81],"progress":[82,220],"the":[84,107,212],"field":[85],"by":[86],"focusing":[87],"marginal":[89],"improvements":[90],"IID":[92,190],"rather":[94],"than":[95],"broader,":[97],"more":[98],"demanding":[99,214],"challenges.":[100],"To":[101,145],"overcome":[102],"this,":[103],"we":[104,152],"introduce":[105,153],"BeyondArena,":[106],"first":[108],"unified":[109,147],"holistic":[110],"that":[115,180],"supports":[116],"task":[118],"types":[119,133],"(IID,":[120],"temporal,":[121],"grouped),":[122],"sample":[124],"size":[125],"feature":[127,132],"dimensionality":[128],"scales,":[129],"with":[130,136],"(with":[134],"text,":[135],"high":[137],"cardinality)":[138],"from":[139],"broad":[141],"range":[142],"of":[143],"disciplines.":[144],"enable":[146],"benchmarking":[148],"beyond":[149],"Data":[154],"Foundry,":[155],"Python":[157],"framework":[158],"metadata":[160],"schema":[161],"curating":[163],"learning.":[169],"Our":[170],"results":[171],"11":[173],"142":[176],"curated":[177],"show":[179],"existing":[181],"excel":[185],"tiny-":[187],"medium-sized":[189],"data,":[191,218],"while":[192],"traditional":[193],"tree-based":[194],"deep":[196],"still":[199],"dominate":[200],"non-IID,":[202],"large,":[203],"high-dimensional":[205],"datasets.":[206],"BeyondArena":[207],"guides":[208],"research":[210],"challenges":[215],"enabling":[219],"towards":[221],"truly":[222],"foundational":[223],"models.":[225]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-01T00:00:00"}
