{"id":"https://openalex.org/W7160933577","doi":"https://doi.org/10.48550/arxiv.2605.10171","title":"When Reviews Disagree: Fine-Grained Contradiction Analysis in Scientific Peer Reviews","display_name":"When Reviews Disagree: Fine-Grained Contradiction Analysis in Scientific Peer Reviews","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160933577","doi":"https://doi.org/10.48550/arxiv.2605.10171"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.10171","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10171","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.2605.10171","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135939828","display_name":"Sandeep Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Sandeep","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082096173","display_name":"Yash Kamdar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kamdar, Yash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135996063","display_name":"Abid Hossain","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hossain, Abid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107544574","display_name":"Bharti Kumari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumari, Bharti","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050294127","display_name":"Tanik Saikh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saikh, Tanik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135980817","display_name":"Asif Ekbal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ekbal, Asif","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/T13274","display_name":"Expert finding and Q&A systems","score":0.36660000681877136,"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/T13274","display_name":"Expert finding and Q&A systems","score":0.36660000681877136,"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/T10028","display_name":"Topic Modeling","score":0.2971999943256378,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.1136000007390976,"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/contradiction","display_name":"Contradiction","score":0.9136999845504761},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6826000213623047},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.5684999823570251},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.48590001463890076},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.40299999713897705},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.3765000104904175},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3580999970436096}],"concepts":[{"id":"https://openalex.org/C2776728590","wikidata":"https://www.wikidata.org/wiki/Q363948","display_name":"Contradiction","level":2,"score":0.9136999845504761},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6826000213623047},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.5684999823570251},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5557000041007996},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.5065000057220459},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.48590001463890076},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.41839998960494995},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3815999925136566},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C204434341","wikidata":"https://www.wikidata.org/wiki/Q357789","display_name":"Adjudication","level":2,"score":0.3497999906539917},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C2781083858","wikidata":"https://www.wikidata.org/wiki/Q17327049","display_name":"Scientific literature","level":2,"score":0.3034000098705292},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.2964000105857849},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.29280000925064087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2915000021457672},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2856000065803528},{"id":"https://openalex.org/C7991579","wikidata":"https://www.wikidata.org/wiki/Q17955","display_name":"Criticism","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.26600000262260437}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.10171","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10171","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.2605.10171","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10171","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Scientific":[0],"peer":[1,74],"reviews":[2,75],"frequently":[3],"contain":[4],"conflicting":[5],"expert":[6],"judgments,":[7],"and":[8,21,26,49,82,124,130,150,166,174],"the":[9,46,53],"increasing":[10],"scale":[11],"of":[12,55,66,98],"conference":[13],"submissions":[14],"makes":[15],"it":[16],"challenging":[17],"for":[18],"Area":[19],"Chairs":[20],"editors":[22],"to":[23,126],"reliably":[24],"identify":[25],"interpret":[27],"such":[28],"disagreements.":[29],"Existing":[30],"approaches":[31],"typically":[32],"frame":[33],"reviewer":[34,67,128],"disagreement":[35,85],"as":[36],"binary":[37],"contradiction":[38,68,79,103,148],"detection":[39],"over":[40,72],"isolated":[41],"sentence":[42],"pairs,":[43],"abstracting":[44],"away":[45],"review-level":[47],"context":[48],"obscuring":[50],"differences":[51],"in":[52,152,170],"severity":[54],"evaluative":[56],"conflict.":[57],"In":[58],"this":[59,90],"work,":[60],"we":[61,92,137],"introduce":[62],"a":[63,113,142,153],"fine-grained":[64],"formulation":[65],"analysis":[69],"that":[70,117,146,160],"operates":[71],"full":[73],"by":[76],"explicitly":[77],"identifying":[78],"evidence":[80,120,149,172],"spans":[81],"assigning":[83],"graded":[84,106],"intensity":[86,107,151,175],"scores.":[87],"To":[88,133],"support":[89,134],"task,":[91],"present":[93],"RevCI,":[94],"an":[95],"expert-annotated":[96],"benchmark":[97],"peer-review":[99],"pairs":[100],"with":[101,105],"evidence-level":[102],"annotations":[104],"labels.":[108],"We":[109],"further":[110],"propose":[111],"IMPACT,":[112],"structured":[114],"multi-agent":[115,168],"framework":[116],"integrates":[118],"aspect-conditioned":[119],"extraction,":[121],"deliberative":[122],"reasoning,":[123],"adjudication":[125],"model":[127,145],"contradictions":[129],"their":[131],"intensity.":[132],"efficient":[135],"deployment,":[136],"distill":[138],"IMPACT":[139,161],"into":[140],"TIDE,":[141],"small":[143],"language":[144],"predicts":[147],"single":[154],"forward":[155],"pass.":[156],"Experimental":[157],"results":[158],"show":[159],"substantially":[162],"outperforms":[163],"strong":[164],"single-agent":[165],"generic":[167],"baselines":[169],"both":[171],"identification":[173],"agreement,":[176],"while":[177],"TIDE":[178],"achieves":[179],"competitive":[180],"performance":[181],"at":[182],"significantly":[183],"lower":[184],"inference":[185],"cost.":[186]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-13T00:00:00"}