{"id":"https://openalex.org/W7140420669","doi":"https://doi.org/10.48550/arxiv.2603.23934","title":"Revealing Multi-View Hallucination in Large Vision-Language Models","display_name":"Revealing Multi-View Hallucination in Large Vision-Language Models","publication_year":2026,"publication_date":"2026-03-25","ids":{"openalex":"https://openalex.org/W7140420669","doi":"https://doi.org/10.48550/arxiv.2603.23934"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.23934","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23934","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.2603.23934","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024950536","display_name":"W. D. Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Wooje","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130688749","display_name":"Insu Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Insu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100379496","display_name":"Soo-Hyun Kim","orcid":"https://orcid.org/0009-0000-3054-2394"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Soohyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130712313","display_name":"Jaeyun Jang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jang, Jaeyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120317262","display_name":"Minyoung Noh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Noh, Minyoung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130681229","display_name":"Kyuhong Shim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shim, Kyuhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5076075267","display_name":"Byonghyo Shim","orcid":"https://orcid.org/0000-0001-5051-1763"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shim, Byonghyo","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.4325999915599823,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.4325999915599823,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.04969999939203262,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.04729999974370003,"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/decoding-methods","display_name":"Decoding methods","score":0.6474000215530396},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.6279000043869019},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.560699999332428},{"id":"https://openalex.org/keywords/interference","display_name":"Interference (communication)","score":0.4194999933242798},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.414900004863739},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3646000027656555},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3483999967575073},{"id":"https://openalex.org/keywords/phenomenon","display_name":"Phenomenon","score":0.3366999924182892}],"concepts":[{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6474000215530396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6420999765396118},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.6279000043869019},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6017000079154968},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.560699999332428},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.4194999933242798},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3781000077724457},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.3675000071525574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3483999967575073},{"id":"https://openalex.org/C50335755","wikidata":"https://www.wikidata.org/wiki/Q483247","display_name":"Phenomenon","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C2779200073","wikidata":"https://www.wikidata.org/wiki/Q18395575","display_name":"Visual masking","level":4,"score":0.3046000003814697},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3037000000476837},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3034000098705292},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27399998903274536},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2727999985218048},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C46312422","wikidata":"https://www.wikidata.org/wiki/Q11024","display_name":"Communication","level":1,"score":0.2603999972343445}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.23934","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23934","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.2603.23934","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.23934","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":{"Large":[0],"vision-language":[1],"models":[2],"(LVLMs)":[3],"are":[4],"increasingly":[5],"being":[6],"applied":[7],"to":[8,70,122],"multi-view":[9,39],"image":[10],"inputs":[11],"captured":[12],"from":[13,30],"diverse":[14],"viewpoints.":[15],"However,":[16],"despite":[17],"this":[18,44,83],"growing":[19],"use,":[20],"current":[21],"LVLMs":[22,68],"often":[23],"confuse":[24],"or":[25,33,79],"mismatch":[26],"visual":[27,73,98],"information":[28],"originating":[29],"different":[31],"instances":[32],"viewpoints,":[34],"a":[35,49,92],"phenomenon":[36],"we":[37,46,85],"term":[38],"hallucination.":[40],"To":[41,81],"systematically":[42],"analyze":[43],"problem,":[45],"construct":[47],"MVH-Bench,":[48],"benchmark":[50],"comprising":[51],"4.8k":[52],"question-answer":[53],"pairs":[54],"targeting":[55],"two":[56],"types":[57],"of":[58,135],"hallucination:":[59],"cross-instance":[60],"and":[61,112,124],"cross-view.":[62],"Empirical":[63],"results":[64],"show":[65],"that":[66,96,115],"recent":[67],"struggle":[69],"correctly":[71],"associate":[72],"evidence":[74],"with":[75,110],"its":[76],"corresponding":[77],"instance":[78],"viewpoint.":[80],"overcome":[82],"limitation,":[84],"propose":[86],"Reference":[87],"Shift":[88],"Contrastive":[89],"Decoding":[90],"(RSCD),":[91],"training-free":[93],"decoding":[94],"technique":[95],"suppresses":[97],"interference":[99],"by":[100,120],"generating":[101],"negative":[102],"logits":[103],"through":[104],"attention":[105],"masking.":[106],"Experiments":[107],"on":[108],"MVH-Bench":[109],"Qwen2.5-VL":[111],"LLaVA-OneVision":[113],"demonstrate":[114],"RSCD":[116],"consistently":[117],"improves":[118],"performance":[119],"up":[121],"21.1":[123],"34.6":[125],"points":[126],"over":[127],"existing":[128],"hallucination":[129],"mitigation":[130],"methods,":[131],"highlighting":[132],"the":[133],"effectiveness":[134],"our":[136],"approach.":[137]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-27T00:00:00"}