{"id":"https://openalex.org/W7164200813","doi":"https://doi.org/10.48550/arxiv.2606.10400","title":"Do Vision-Language Models See or Guess? Measuring and Reducing Textual-Prior Reliance with a Phrasing-Controlled Benchmark","display_name":"Do Vision-Language Models See or Guess? Measuring and Reducing Textual-Prior Reliance with a Phrasing-Controlled Benchmark","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164200813","doi":"https://doi.org/10.48550/arxiv.2606.10400"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.10400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10400","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":null,"license_id":null,"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.2606.10400","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109818641","display_name":"Pratham Singla","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singla, Pratham","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138381826","display_name":"Shivank Garg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garg, Shivank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079452417","display_name":"Vihan Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singh, Vihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138333196","display_name":"Paras Chopra","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chopra, Paras","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":0.7760000228881836,"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":0.7760000228881836,"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":0.025100000202655792,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0210999995470047,"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/benchmark","display_name":"Benchmark (surveying)","score":0.9218000173568726},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.6324999928474426},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.6133999824523926},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.3961000144481659},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.2937000095844269}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.9218000173568726},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.654699981212616},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.6324999928474426},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.6133999824523926},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5896000266075134},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4754999876022339},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3961000144481659},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3357999920845032},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.302700012922287},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27459999918937683}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.10400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10400","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.10400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10400","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Vision-language":[0],"models":[1,117,131,145],"(VLMs)":[2],"are":[3],"increasingly":[4],"deployed":[5],"where":[6],"answers":[7],"must":[8],"follow":[9],"from":[10,20,33,103],"what":[11],"is":[12,56,93,100,137,205],"in":[13],"the":[14,23,34,66,83,94,104,126,143,179],"image,":[15],"yet":[16],"they":[17],"often":[18],"answer":[19],"textual":[21,161],"priors,":[22],"question's":[24],"phrasing":[25,88],"together":[26],"with":[27,59],"memorized":[28],"world":[29],"knowledge,":[30],"rather":[31,89],"than":[32,90],"image":[35,55,91,105],"itself,":[36],"which":[37,141],"inflates":[38],"benchmark":[39,72,111],"scores":[40],"and":[41,77,129,163,182,207],"yields":[42,189],"confident":[43],"but":[44],"ungrounded":[45],"answers.":[46],"Existing":[47],"benchmarks":[48],"rarely":[49],"isolate":[50],"this":[51],"behavior,":[52],"since":[53],"each":[54],"usually":[57],"paired":[58],"a":[60,70,138,174,186,199],"single":[61],"fixed":[62],"question.":[63],"To":[64],"measure":[65],"reliance,":[67],"we":[68],"build":[69],"540-image":[71],"across":[73,192],"six":[74],"reasoning":[75],"categories":[76],"generate":[78],"four":[79,194],"question":[80],"variants":[81,195],"over":[82],"same":[84],"images,":[85],"so":[86],"that":[87,171,196],"content":[92],"controlled":[95],"variable.":[96],"The":[97],"hardest":[98,127],"variant":[99,175],"written":[101],"directly":[102],"to":[106,118,146,151,198],"minimize":[107],"text":[108],"leakage.":[109],"We":[110],"eleven":[112],"VLMs":[113],"spanning":[114],"small":[115,187],"open-weight":[116,144],"large":[119],"closed-source":[120],"systems:":[121],"every":[122],"model":[123],"degrades":[124],"on":[125],"variant,":[128],"open":[130],"fall":[132],"furthest.":[133],"Our":[134],"central":[135],"diagnostic":[136],"no-image":[139],"ablation,":[140],"collapses":[142],"their":[147],"text-only":[148],"floor":[149],"(1":[150],"9":[152],"percent).":[153],"Three":[154],"further":[155],"analyses,":[156],"LLM-rated":[157],"difficulty,":[158],"low":[159],"base-to-final":[160],"similarity,":[162],"human":[164],"re-annotation,":[165],"corroborate":[166],"genuine":[167],"image-dependence.":[168],"In-context":[169],"exemplars":[170],"match":[172],"how":[173],"was":[176],"built":[177],"recover":[178],"most":[180],"accuracy,":[181],"GRPO":[183],"post-training":[184],"of":[185],"VLM":[188],"consistent":[190],"gains":[191],"all":[193],"transfer":[197],"held-out":[200],"out-of-distribution":[201],"set.":[202],"Textual-prior":[203],"reliance":[204],"measurable":[206],"partly":[208],"trainable":[209],"away.":[210]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-11T00:00:00"}