{"id":"https://openalex.org/W7161718220","doi":"https://doi.org/10.48550/arxiv.2605.17962","title":"FinDocMRE: A Benchmark for Document-Level Financial Multimodal Reasoning Evaluation","display_name":"FinDocMRE: A Benchmark for Document-Level Financial Multimodal Reasoning Evaluation","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161718220","doi":"https://doi.org/10.48550/arxiv.2605.17962"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17962","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17962","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.2605.17962","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101074073","display_name":"J. Zhu","orcid":"https://orcid.org/0009-0006-2049-795X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Jiayong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047636121","display_name":"Jiangtong Li","orcid":"https://orcid.org/0000-0003-3873-4053"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jiangtong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136472539","display_name":"Jinru Ding","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ding, Jinru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136463235","display_name":"Dawei Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Dawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136486462","display_name":"Jie Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136468637","display_name":"Feng Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Feng","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.6381000280380249,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.6381000280380249,"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/T10028","display_name":"Topic Modeling","score":0.03139999881386757,"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":0.023499999195337296,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.8158000111579895},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5429999828338623},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.527400016784668},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4722000062465668},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.4465999901294708},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.37220001220703125}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8158000111579895},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7010999917984009},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6169999837875366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.557699978351593},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5429999828338623},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.527400016784668},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4722000062465668},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.4465999901294708},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.38580000400543213},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.37220001220703125},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3700000047683716},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3416000008583069},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3127000033855438},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.29120001196861267},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.27000001072883606}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17962","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17962","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.2605.17962","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17962","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":{"While":[0],"Large":[1],"Multimodal":[2],"Models":[3],"(LMMs)":[4],"excel":[5],"in":[6,12,123,147],"general":[7],"visual":[8,125,158],"tasks,":[9,142],"their":[10],"deployment":[11],"specialized":[13],"financial":[14,37,52,90,171],"contexts":[15],"remains":[16],"insufficient.":[17],"Existing":[18],"benchmarks":[19],"prioritize":[20],"isolated":[21],"charts,":[22],"often":[23],"overlooking":[24],"the":[25,57,82,168],"need":[26],"to":[27,93,166],"integrate":[28],"data":[29],"from":[30,88],"text,":[31],"tables,":[32],"and":[33,74,97,156,177],"images":[34],"within":[35,130],"comprehensive":[36],"documents.":[38],"To":[39],"address":[40],"this":[41],"limitation,":[42],"we":[43,135],"introduce":[44],"FINDOCMRE,":[45],"a":[46,60,137,163],"multi-image":[47,95],"document-level":[48,98],"benchmark":[49,83,165],"designed":[50,92],"for":[51],"multimodal":[53],"reasoning.":[54,178],"We":[55],"construct":[56],"dataset":[58],"via":[59],"semi-automated":[61],"pipeline":[62],"that":[63,112],"combines":[64],"Visual-Centric":[65],"Generation":[66],"with":[67,107,127,153],"Expert":[68],"Verification,":[69],"thereby":[70],"minimizing":[71],"text":[72],"bias":[73],"ensuring":[75],"high":[76],"annotation":[77],"quality.":[78],"Spanning":[79],"twelve":[80],"domains,":[81],"comprises":[84],"12,207":[85],"samples":[86],"derived":[87],"2,878":[89],"reports,":[91],"evaluate":[94],"processing":[96],"understanding":[99],"across":[100,141],"five":[101],"distinct":[102],"task":[103],"types.":[104],"Extensive":[105],"experiments":[106],"eleven":[108],"representative":[109],"LMMs":[110,172],"reveal":[111],"no":[113],"model":[114],"surpasses":[115],"an":[116],"overall":[117],"score":[118],"of":[119,170],"65,":[120],"highlighting":[121],"challenges":[122],"integrating":[124],"grounding":[126],"logical":[128],"reasoning":[129],"complex":[131],"document":[132,175],"environments.":[133],"Specifically,":[134],"observe":[136],"significant":[138],"performance":[139],"divergence":[140],"where":[143],"models":[144],"exhibit":[145],"proficiency":[146],"semantic":[148],"narrative":[149],"construction":[150],"but":[151],"struggle":[152],"numerical":[154],"estimation":[155],"cross-page":[157],"grounding.":[159],"FINDOCMRE":[160],"serves":[161],"as":[162],"rigorous":[164],"guide":[167],"evolution":[169],"towards":[173],"expert-level":[174],"analysis":[176]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}
