{"id":"https://openalex.org/W7171543571","doi":"https://doi.org/10.48550/arxiv.2607.22633","title":"Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering","display_name":"Evolving from Lessons: Skill-Augmented Table Graph Reasoning for Operation-wise Table Question Answering","publication_year":2026,"publication_date":"2026-06-20","ids":{"openalex":"https://openalex.org/W7171543571","doi":"https://doi.org/10.48550/arxiv.2607.22633"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.22633","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.22633","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.2607.22633","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5087343203","display_name":"Guixin Su","orcid":"https://orcid.org/0009-0009-3239-1089"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Guixin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099166967","display_name":"Qiankun Pi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pi, Qiankun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143799452","display_name":"Mayi Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Mayi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143810488","display_name":"Wenli Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Wenli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143828761","display_name":"Ming Zhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143846505","display_name":"Yuanyuan Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yuanyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143825065","display_name":"Jiawei Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Jiawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5143749037","display_name":"Tieyun Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Tieyun","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/T10028","display_name":"Topic Modeling","score":0.23669999837875366,"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/T10028","display_name":"Topic Modeling","score":0.23669999837875366,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.1881999969482422,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.14079999923706055,"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/table","display_name":"Table (database)","score":0.6456999778747559},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.6010000109672546},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.580299973487854},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5171999931335449},{"id":"https://openalex.org/keywords/graph-traversal","display_name":"Graph traversal","score":0.49320000410079956},{"id":"https://openalex.org/keywords/tree-traversal","display_name":"Tree traversal","score":0.4666000008583069},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.35519999265670776}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7502999901771545},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.6456999778747559},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.6010000109672546},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.580299973487854},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5171999931335449},{"id":"https://openalex.org/C96333769","wikidata":"https://www.wikidata.org/wiki/Q907955","display_name":"Graph traversal","level":3,"score":0.49320000410079956},{"id":"https://openalex.org/C140745168","wikidata":"https://www.wikidata.org/wiki/Q1210082","display_name":"Tree traversal","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4569999873638153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38499999046325684},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.35519999265670776},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.34540000557899475},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.3160000145435333},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C162319229","wikidata":"https://www.wikidata.org/wiki/Q175263","display_name":"Data structure","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C176225458","wikidata":"https://www.wikidata.org/wiki/Q595971","display_name":"Graph database","level":3,"score":0.2890999913215637},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2651999890804291},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2612000107765198},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.22633","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.22633","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.2607.22633","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.22633","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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.8092949986457825}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Table":[0,119],"Question":[1],"Answering":[2],"(TableQA)":[3],"aims":[4],"to":[5,9,94,146,162],"reason":[6,101,164],"over":[7],"tables":[8,77,131],"answer":[10],"user":[11],"queries.":[12],"Existing":[13],"research":[14],"treats":[15],"all":[16],"questions":[17],"uniformly":[18],"and":[19,42,60,66,84,142,177,205,214,220],"evaluates":[20],"solely":[21],"through":[22],"overall":[23,204],"accuracy,":[24],"obscuring":[25],"a":[26,50,56,91,117,159],"critical":[27],"reality":[28],"that":[29,194],"LLMs":[30,140],"excel":[31],"at":[32],"simple":[33],"lookups":[34],"yet":[35],"struggle":[36],"with":[37,55,135,199],"complex":[38,95],"operations":[39],"like":[40],"aggregation":[41],"arithmetic.":[43],"To":[44,111],"reveal":[45],"this":[46],"disparity,":[47],"we":[48,115],"introduce":[49],"novel":[51],"\\emph{Operation-wise":[52],"TableQA}":[53],"task":[54],"fine-grained":[57],"question":[58],"taxonomy":[59],"release":[61],"two":[62],"datasets":[63],"named":[64],"WikiTQ-ow":[65],"TabFact-ow":[67],"for":[68,71,124,150,180],"evaluation.":[69],"As":[70],"modeling":[72],"bottlenecks,":[73],"existing":[74],"methods":[75],"flatten":[76],"into":[78,166],"linearized":[79],"texts,":[80],"disrupting":[81],"inherent":[82],"structures":[83],"inducing":[85],"the":[86,188],"``lost-in-the-middle''":[87],"issue,":[88],"which":[89],"poses":[90],"primary":[92],"barrier":[93],"cross-row":[96],"reasoning.":[97,127,153],"Moreover,":[98],"they":[99],"typically":[100],"from":[102],"scratch,":[103],"neglecting":[104],"reusable":[105],"patterns":[106],"shared":[107],"across":[108],"similar":[109],"operations.":[110],"address":[112],"these":[113],"limitations,":[114],"propose":[116],"Skill-augmented":[118],"Graph":[120],"Reasoning":[121],"(SkillTGR)":[122],"framework":[123],"self-evolving":[125],"structured":[126],"Specifically,":[128],"SkillTGR":[129,157,195],"represents":[130],"as":[132],"attributed":[133],"graphs":[134],"explicit":[136],"row-column-cell":[137],"structures,":[138],"where":[139],"plan":[141],"execute":[143],"dynamic":[144],"chains":[145],"retrieve":[147],"evidence":[148],"subgraphs":[149],"graph":[151,184],"traversal":[152],"Based":[154],"on":[155],"this,":[156],"builds":[158],"hierarchical":[160],"SkillBank":[161],"distill":[163],"trajectories":[165],"abstract":[167],"skills":[168,179],"under":[169],"cognitive":[170],"heuristics,":[171],"then":[172],"hybrid":[173],"retrieves":[174],"both":[175],"successful":[176],"failed":[178],"contrastive":[181],"augmented":[182],"table":[183],"reasoning,":[185],"thereby":[186],"enabling":[187],"continual":[189],"self-evolution.":[190],"Extensive":[191],"experiments":[192],"demonstrate":[193],"achieves":[196],"superior":[197],"performance":[198],"an":[200],"average":[201],"of":[202],"5.91\\%":[203],"6.03\\%":[206],"operation-wise":[207],"improvement,":[208],"also":[209],"reducing":[210],"19.76\\%":[211],"token":[212],"consumption":[213],"27.64\\%":[215],"inference":[216],"latency.":[217],"Our":[218],"codes":[219],"data":[221],"will":[222],"be":[223],"released":[224],"upon":[225],"publication.":[226]},"counts_by_year":[],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2026-07-29T00:00:00"}