{"id":"https://openalex.org/W7125937094","doi":"https://doi.org/10.48550/arxiv.2601.19193","title":"CoReTab: Improving Multimodal Table Understanding with Code-driven Reasoning","display_name":"CoReTab: Improving Multimodal Table Understanding with Code-driven Reasoning","publication_year":2026,"publication_date":"2026-01-27","ids":{"openalex":"https://openalex.org/W7125937094","doi":"https://doi.org/10.48550/arxiv.2601.19193"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2601.19193","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103235650","display_name":"Van-Quang Nguyen","orcid":"https://orcid.org/0009-0007-1831-5763"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nguyen, Van-Quang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5108619681","display_name":"Takayuki Okatani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Okatani, Takayuki","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103235650"],"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":0.19009999930858612,"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":0.19009999930858612,"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/T10601","display_name":"Handwritten Text Recognition Techniques","score":0.1826000064611435,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11948","display_name":"Machine Learning in Materials Science","score":0.07180000096559525,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8273000121116638},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.6980000138282776},{"id":"https://openalex.org/keywords/verifiable-secret-sharing","display_name":"Verifiable secret sharing","score":0.6011999845504761},{"id":"https://openalex.org/keywords/executable","display_name":"Executable","score":0.5939000248908997},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.5044000148773193},{"id":"https://openalex.org/keywords/model-based-reasoning","display_name":"Model-based reasoning","score":0.42669999599456787},{"id":"https://openalex.org/keywords/automated-reasoning","display_name":"Automated reasoning","score":0.4011000096797943},{"id":"https://openalex.org/keywords/abductive-reasoning","display_name":"Abductive reasoning","score":0.39629998803138733}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8273000121116638},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7545999884605408},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.6980000138282776},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.6011999845504761},{"id":"https://openalex.org/C160145156","wikidata":"https://www.wikidata.org/wiki/Q778586","display_name":"Executable","level":2,"score":0.5939000248908997},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.529699981212616},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.5044000148773193},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.42669999599456787},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4000999927520752},{"id":"https://openalex.org/C166088908","wikidata":"https://www.wikidata.org/wiki/Q308495","display_name":"Abductive reasoning","level":2,"score":0.39629998803138733},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.38429999351501465},{"id":"https://openalex.org/C139458680","wikidata":"https://www.wikidata.org/wiki/Q12184942","display_name":"Interoperation","level":3,"score":0.3716000020503998},{"id":"https://openalex.org/C139458680","wikidata":"https://www.wikidata.org/wiki/Q12184942","display_name":"Interoperation","level":3,"score":0.3714999854564667},{"id":"https://openalex.org/C9616225","wikidata":"https://www.wikidata.org/wiki/Q3929429","display_name":"Semantic reasoner","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.31949999928474426},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C83725634","wikidata":"https://www.wikidata.org/wiki/Q7268699","display_name":"Qualitative reasoning","level":2,"score":0.28610000014305115},{"id":"https://openalex.org/C103057564","wikidata":"https://www.wikidata.org/wiki/Q4751139","display_name":"Analytic reasoning","level":3,"score":0.2847999930381775},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2711000144481659},{"id":"https://openalex.org/C56949724","wikidata":"https://www.wikidata.org/wiki/Q219079","display_name":"Truth table","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C172967692","wikidata":"https://www.wikidata.org/wiki/Q747762","display_name":"Decision table","level":3,"score":0.25609999895095825}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2601.19193","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2601.19193","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.19193","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2601.19193","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","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":{"Existing":[0],"datasets":[1,23],"for":[2,146],"multimodal":[3,151],"table":[4,105,111,152],"understanding,":[5],"such":[6],"as":[7,139],"MMTab,":[8],"primarily":[9],"provide":[10],"short":[11],"factual":[12],"answers":[13],"without":[14],"explicit":[15],"multi-step":[16,61,148],"reasoning":[17,49,62,133,149],"supervision.":[18],"Models":[19],"trained":[20,97],"on":[21,98],"these":[22,37],"often":[24],"generate":[25],"brief":[26],"responses":[27],"that":[28,51],"offers":[29],"insufficient":[30],"accuracy":[31],"and":[32,55,84,110,122,131,142],"limited":[33],"interpretability":[34],"into":[35],"how":[36],"models":[38],"arrive":[39],"at":[40],"the":[41,68,94],"final":[42],"answer.":[43],"We":[44,92],"introduce":[45],"CoReTab,":[46],"a":[47,73,89,140],"code-driven":[48],"framework":[50,145],"produces":[52],"scalable,":[53],"interpretable,":[54],"automatically":[56],"verifiable":[57,132],"annotations":[58],"by":[59],"coupling":[60],"with":[63],"executable":[64],"Python":[65],"code.":[66],"Using":[67],"CoReTab":[69,99,138],"framework,":[70],"we":[71],"curate":[72],"dataset":[74],"of":[75,119],"115K":[76],"verified":[77],"samples":[78],"averaging":[79],"529":[80],"tokens":[81],"per":[82],"response":[83],"fine-tune":[85],"open-source":[86],"MLLMs":[87],"through":[88],"three-stage":[90],"pipeline.":[91],"evaluate":[93],"resulting":[95],"model":[96,115],"across":[100],"17":[101],"MMTab":[102],"benchmarks":[103],"spanning":[104],"question":[106],"answering,":[107],"fact":[108],"verification,":[109],"structure":[112],"understanding.":[113,153],"Our":[114],"achieves":[116],"significant":[117],"gains":[118],"+6.2%,":[120],"+5.7%,":[121],"+25.6%,":[123],"respectively,":[124],"over":[125],"MMTab-trained":[126],"baselines,":[127],"while":[128],"producing":[129],"transparent":[130],"traces.":[134],"These":[135],"results":[136],"establish":[137],"robust":[141],"generalizable":[143],"supervision":[144],"improving":[147],"in":[150]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-01-29T00:00:00"}
