{"id":"https://openalex.org/W7164030619","doi":"https://doi.org/10.48550/arxiv.2606.09323","title":"TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders","display_name":"TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7164030619","doi":"https://doi.org/10.48550/arxiv.2606.09323"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.09323","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09323","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.2606.09323","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138233573","display_name":"Wei Pang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050075702","display_name":"Xiangru Jian","orcid":"https://orcid.org/0009-0004-7138-7078"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jian, Xiangru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138222289","display_name":"Hehan Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Hehan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138241281","display_name":"Zhixuan Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Zhixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138235290","display_name":"Alex Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xue, Alex","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138202923","display_name":"Jinyang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jinyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138240626","display_name":"Zhengyuan Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Zhengyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138244278","display_name":"Xinjian Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Xinjian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138274983","display_name":"Hao Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138239665","display_name":"Chao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120780403","display_name":"Reynold Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Reynold","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138269246","display_name":"M. Tamer \u00d6zsu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"\u00d6zsu, M. Tamer","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138229419","display_name":"Tianshu Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Tianshu","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/T11800","display_name":"User Authentication and Security Systems","score":0.16609999537467957,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11800","display_name":"User Authentication and Security Systems","score":0.16609999537467957,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.09229999780654907,"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/T10260","display_name":"Software Engineering Research","score":0.039900001138448715,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/encoder","display_name":"Encoder","score":0.8021000027656555},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6866999864578247},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.6279000043869019},{"id":"https://openalex.org/keywords/linkage","display_name":"Linkage (software)","score":0.5526000261306763},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5329999923706055},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5126000046730042},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5047000050544739},{"id":"https://openalex.org/keywords/downstream","display_name":"Downstream (manufacturing)","score":0.49790000915527344},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4959000051021576}],"concepts":[{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.8021000027656555},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7544000148773193},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6866999864578247},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.6279000043869019},{"id":"https://openalex.org/C31266012","wikidata":"https://www.wikidata.org/wiki/Q6554340","display_name":"Linkage (software)","level":3,"score":0.5526000261306763},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5329999923706055},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5126000046730042},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5047000050544739},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.49790000915527344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4975999891757965},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4959000051021576},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.44029998779296875},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41510000824928284},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.34380000829696655},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3237000107765198},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3156000077724457},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2946999967098236},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C111696304","wikidata":"https://www.wikidata.org/wiki/Q2303697","display_name":"Sorting","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.09323","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09323","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.2606.09323","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09323","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":{"Tabular":[0],"encoders":[1,142],"are":[2,15,125],"usually":[3],"evaluated":[4],"inside":[5],"task-specific":[6],"end-to-end":[7,203],"pipelines,":[8],"so":[9],"models":[10,115],"from":[11,109],"different":[12,171],"training":[13,172],"paradigms":[14],"difficult":[16],"to":[17],"compare":[18],"directly":[19],"even":[20],"when":[21],"they":[22],"operate":[23],"on":[24,145,206],"similar":[25],"tabular":[26,33,152,227],"signals.":[27],"We":[28],"introduce":[29],"TRL-Bench,":[30],"a":[31,104,135,198,218],"multi-granular":[32],"representation":[34],"learning":[35],"(TRL)":[36],"benchmark":[37,86],"that":[38,121],"standardizes":[39],"cross-paradigm":[40],"representation-level":[41],"evaluation:":[42],"each":[43],"encoder":[44,127],"exports":[45],"row-,":[46],"column-,":[47],"or":[48],"table":[49],"embeddings":[50],"through":[51],"its":[52],"supported":[53],"wrapper,":[54],"and":[55,68,88,103,116,167,201,234],"shared":[56,230],"lightweight":[57],"heads":[58],"probe":[59],"them":[60],"across":[61],"three":[62,76],"suites:":[63],"TRL-CTbench":[64],"(column/table),":[65],"TRL-Rbench":[66],"(row),":[67],"TRL-DLTE":[69],"(compositional":[70],"Data-Lake":[71],"Table":[72],"Enrichment":[73],"spanning":[74],"all":[75],"granularities).":[77],"To":[78],"support":[79],"this":[80],"standardized":[81],"setting,":[82],"we":[83],"release":[84],"curated":[85],"assets":[87],"task":[89],"reformulations,":[90],"including":[91],"50":[92],"OpenML":[93],"tables":[94],"with":[95,147,160,174,180],"123":[96],"verified":[97],"targets,":[98],"16":[99,117],"row-pair":[100],"linkage":[101,169,176],"rewrites,":[102],"47,772-table":[105],"DLTE":[106,185],"lake":[107],"derived":[108],"1,379":[110],"parent":[111],"tables.":[112],"Across":[113],"20":[114],"tasks,":[118],"TRL-Bench":[119,216],"shows":[120],"once":[122],"downstream":[123,231],"conditions":[124],"standardized,":[126],"quality":[128,204],"is":[129],"capability-specific":[130],"rather":[131,195,210],"than":[132,196,211],"captured":[133],"by":[134],"single":[136,199],"leaderboard.":[137],"In":[138,163,187],"TRL-CTbench,":[139],"generic":[140],"text":[141],"often":[143],"lead":[144],"tasks":[146],"strong":[148],"surface-text":[149],"signal,":[150],"while":[151],"specialists":[153,194],"win":[154],"where":[155],"their":[156],"pretraining":[157],"objective":[158],"aligns":[159],"the":[161,181,189],"task.":[162],"TRL-Rbench,":[164],"within-table":[165],"prediction":[166],"cross-table":[168],"favor":[170],"regimes,":[173],"atomic":[175],"performance":[177],"correlating":[178],"strongly":[179],"row-matching":[182],"stage":[183],"of":[184],"pipelines.":[186],"TRL-DLTE,":[188],"strongest":[190],"pipelines":[191],"combine":[192],"capability-matched":[193],"reuse":[197],"encoder,":[200],"top":[202],"depends":[205],"non-additive":[207],"compositional":[208],"fit":[209],"per-stage":[212],"marginal":[213],"rank":[214],"alone.":[215],"provides":[217],"common":[219],"protocol":[220],"for":[221],"measuring":[222],"reusable":[223],"signal":[224],"in":[225],"exported":[226],"representations":[228],"under":[229],"conditions.":[232],"Code":[233],"data:":[235],"https://github.com/LOGO-CUHKSZ/TRL-Bench":[236]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}