{"id":"https://openalex.org/W7165786721","doi":"https://doi.org/10.48550/arxiv.2606.23724","title":"EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering","display_name":"EvidenceLens: A Claim-Evidence Matrix for Auditing Financial Question Answering","publication_year":2026,"publication_date":"2026-06-19","ids":{"openalex":"https://openalex.org/W7165786721","doi":"https://doi.org/10.48550/arxiv.2606.23724"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23724","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","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.2606.23724","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086144706","display_name":"Fengchen Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Fengchen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002774282","display_name":"Xiaotian Ren","orcid":"https://orcid.org/0000-0001-7706-6268"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Xiaotian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011243810","display_name":"Zhengyong Jiang","orcid":"https://orcid.org/0000-0001-8873-4073"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Zhengyong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139223629","display_name":"Zhilu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhilu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134774788","display_name":"\u00c1ngel F. Garc\u00eda-Fern\u00e1ndez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garc\u00eda-Fern\u00e1ndez, \u00c1ngel F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034081162","display_name":"Angelos Stefanidis","orcid":"https://orcid.org/0000-0002-4703-8765"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stefanidis, Angelos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139283588","display_name":"Mian Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Mian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101859990","display_name":"Huakang Li","orcid":"https://orcid.org/0000-0003-1284-1993"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Huakang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139277863","display_name":"Jionglong Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Jionglong","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":0.20059999823570251,"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":0.20059999823570251,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.16359999775886536,"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/T10028","display_name":"Topic Modeling","score":0.1527000069618225,"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/audit","display_name":"Audit","score":0.6402000188827515},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5935999751091003},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5115000009536743},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.49540001153945923},{"id":"https://openalex.org/keywords/analytics","display_name":"Analytics","score":0.4731000065803528},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4302000105381012},{"id":"https://openalex.org/keywords/artifact","display_name":"Artifact (error)","score":0.42329999804496765},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.414000004529953},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.39989998936653137}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6967999935150146},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.6402000188827515},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5935999751091003},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5115000009536743},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.49540001153945923},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4731000065803528},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C2779010991","wikidata":"https://www.wikidata.org/wiki/Q2720909","display_name":"Artifact (error)","level":2,"score":0.42329999804496765},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.414000004529953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4104999899864197},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.41040000319480896},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.39989998936653137},{"id":"https://openalex.org/C2781426361","wikidata":"https://www.wikidata.org/wiki/Q5326940","display_name":"Earnings","level":2,"score":0.3953999876976013},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3619999885559082},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.358599990606308},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.3521000146865845},{"id":"https://openalex.org/C59732488","wikidata":"https://www.wikidata.org/wiki/Q2528440","display_name":"Visual analytics","level":3,"score":0.3434999883174896},{"id":"https://openalex.org/C2777152325","wikidata":"https://www.wikidata.org/wiki/Q108163","display_name":"Proposition","level":2,"score":0.33340001106262207},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.3325999975204468},{"id":"https://openalex.org/C2909264111","wikidata":"https://www.wikidata.org/wiki/Q740419","display_name":"Financial Audit","level":3,"score":0.33219999074935913},{"id":"https://openalex.org/C544937707","wikidata":"https://www.wikidata.org/wiki/Q949180","display_name":"Financial accounting","level":3,"score":0.3172999918460846},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.30889999866485596},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2809999883174896},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2786000072956085},{"id":"https://openalex.org/C163867264","wikidata":"https://www.wikidata.org/wiki/Q4672792","display_name":"Accounting information system","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2721000015735626},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.2680000066757202},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.2605000138282776}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23724","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.23724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23724","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.48403874039649963,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"models":[2],"are":[3],"increasingly":[4],"used":[5],"to":[6,22],"answer":[7,30,68],"questions":[8],"over":[9],"annual":[10],"reports,":[11],"earnings":[12],"decks,":[13],"and":[14,38,45,75,79,88,104,124],"analyst":[15],"notes,":[16],"yet":[17],"their":[18],"outputs":[19],"remain":[20],"difficult":[21],"verify":[23],"in":[24],"high-stakes":[25],"financial":[26,56],"workflows.":[27],"A":[28],"fluent":[29],"can":[31],"blend":[32],"directly":[33],"grounded":[34,149],"statements,":[35],"weak":[36],"synthesis,":[37],"unsupported":[39],"claims":[40,150],"across":[41],"narrative":[42],"text,":[43],"tables,":[44],"charts.":[46],"We":[47],"present":[48],"EvidenceLens,":[49],"a":[50,60,96,115,119,125],"visual":[51,93,136],"analytics":[52],"prototype":[53],"that":[54,100,129,154],"treats":[55],"question":[57],"answering":[58],"as":[59],"claim-evidence":[61,98],"alignment":[62,122],"problem.":[63],"The":[64],"system":[65],"decomposes":[66],"an":[67,134],"into":[69,133],"atomic":[70],"claims,":[71],"summarizes":[72],"support":[73,77,110],"composition":[74],"confidence,":[76],"gaps,":[78],"coordinates":[80],"claim-level":[81],"inspection":[82],"with":[83],"source":[84],"passages,":[85],"table":[86],"cells,":[87],"chart":[89],"regions.":[90],"Its":[91],"core":[92],"representation":[94],"is":[95],"multimodal":[97,121],"matrix":[99],"makes":[101],"coverage,":[102],"contradiction,":[103],"modality":[105],"imbalance":[106],"immediately":[107],"visible.":[108],"To":[109],"reproducibility,":[111],"we":[112,142],"also":[113],"specify":[114],"JSON-based":[116],"artifact":[117],"schema,":[118],"lightweight":[120],"pipeline,":[123],"deterministic":[126],"review-priority":[127],"ranking":[128],"maps":[130],"backend":[131],"signals":[132],"auditable":[135],"structure.":[137],"Through":[138],"representative":[139],"report-auditing":[140],"scenarios,":[141],"show":[143],"how":[144],"EvidenceLens":[145],"helps":[146],"analysts":[147],"distinguish":[148],"from":[151],"overconfident":[152],"synthesis":[153],"conventional":[155],"chat":[156],"interfaces":[157],"flatten.":[158]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-25T00:00:00"}
