{"id":"https://openalex.org/W7161683664","doi":"https://doi.org/10.48550/arxiv.2605.17826","title":"CounterCount: A Diagnostic Framework for Counting Bias in Vision Language Models","display_name":"CounterCount: A Diagnostic Framework for Counting Bias in Vision Language Models","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161683664","doi":"https://doi.org/10.48550/arxiv.2605.17826"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17826","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17826","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.2605.17826","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136461347","display_name":"Reem Alzahrani","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alzahrani, Reem","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136458064","display_name":"Hassan Alshanqiti","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alshanqiti, Hassan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136498778","display_name":"Bushra Bin Hemid","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hemid, Bushra Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082096652","display_name":"Zaid Alyafeai","orcid":"https://orcid.org/0009-0003-1774-5236"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alyafeai, Zaid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033942292","display_name":"Abdelrahman Eldesokey","orcid":"https://orcid.org/0000-0003-3292-7153"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eldesokey, Abdelrahman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136499162","display_name":"Bernard Ghanem","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ghanem, Bernard","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":0.9176999926567078,"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":0.9176999926567078,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.010499999858438969,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.00800000037997961,"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/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.9359999895095825},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5335999727249146},{"id":"https://openalex.org/keywords/counterfactual-conditional","display_name":"Counterfactual conditional","score":0.42820000648498535},{"id":"https://openalex.org/keywords/visual-attention","display_name":"Visual attention","score":0.40619999170303345},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.39570000767707825},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.352400004863739}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.9359999895095825},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6334999799728394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6022999882698059},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5335999727249146},{"id":"https://openalex.org/C71889745","wikidata":"https://www.wikidata.org/wiki/Q1783264","display_name":"Counterfactual conditional","level":3,"score":0.42820000648498535},{"id":"https://openalex.org/C2986089797","wikidata":"https://www.wikidata.org/wiki/Q6501338","display_name":"Visual attention","level":3,"score":0.40619999170303345},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.39570000767707825},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3714999854564667},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3700999915599823},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.337799996137619},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3077000081539154},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C2780878386","wikidata":"https://www.wikidata.org/wiki/Q1659648","display_name":"Visual language","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2928999960422516},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.27889999747276306}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17826","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17826","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.2605.17826","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17826","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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.7338873147964478}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-Language":[0],"Models":[1],"(VLMs)":[2],"excel":[3],"at":[4],"multimodal":[5],"reasoning,":[6],"yet":[7],"it":[8],"remains":[9],"unclear":[10],"whether":[11],"their":[12],"answers":[13],"are":[14,117],"grounded":[15],"in":[16,61],"visual":[17,33,105,125,134,147],"evidence":[18,34,78,106],"or":[19,123],"driven":[20],"by":[21,153],"learned":[22],"language":[23],"and":[24,67,76,166],"world":[25],"priors.":[26],"Counting":[27],"provides":[28,167],"a":[29,40,49,55,138],"precise":[30],"testbed:":[31],"when":[32,103],"conflicts":[35],"with":[36,70],"canonical":[37],"object":[38],"knowledge,":[39],"model":[41],"must":[42],"rely":[43],"on":[44,87,99],"the":[45],"image":[46],"rather":[47],"than":[48],"prototypical":[50],"count.":[51],"We":[52,136],"introduce":[53,137],"CounterCount,":[54],"diagnostic":[56,168],"framework":[57],"for":[58,170],"counterfactual":[59,68,94,150],"counting":[60,151,164],"VLMs,":[62,82],"consisting":[63],"of":[64],"paired":[65],"factual":[66,88],"images":[69,89],"edited":[71],"count-relevant":[72,133],"attributes,":[73],"verified":[74],"answers,":[75],"localized":[77,110],"annotations.":[79],"Evaluating":[80],"recent":[81],"we":[83,112],"find":[84],"strong":[85],"performance":[86],"but":[90,127],"consistent":[91],"degradation":[92],"under":[93],"attribute":[95],"changes,":[96],"indicating":[97],"reliance":[98],"object-level":[100],"priors":[101],"even":[102],"contradictory":[104],"is":[107],"present.":[108],"Using":[109],"annotations,":[111],"show":[113],"that":[114,144],"these":[115],"failures":[116,165],"not":[118],"solely":[119],"due":[120],"to":[121,128,132,155],"missing":[122],"ambiguous":[124],"evidence,":[126],"models":[129],"underweighting":[130],"attention":[131,141],"tokens.":[135],"unified":[139],"inference-time":[140],"modulation":[142],"strategy":[143],"reweights":[145],"selected":[146],"tokens,":[148],"improving":[149],"accuracy":[152],"up":[154],"8%":[156],"across":[157],"multiple":[158],"VLMs.":[159,173],"Overall,":[160],"CounterCount":[161],"exposes":[162],"prior-driven":[163],"insights":[169],"designing":[171],"future":[172]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}