{"id":"https://openalex.org/W7134973734","doi":"https://doi.org/10.48550/arxiv.2603.09326","title":"OddGridBench: Exposing the Lack of Fine-Grained Visual Discrepancy Sensitivity in Multimodal Large Language Models","display_name":"OddGridBench: Exposing the Lack of Fine-Grained Visual Discrepancy Sensitivity in Multimodal Large Language Models","publication_year":2026,"publication_date":"2026-03-10","ids":{"openalex":"https://openalex.org/W7134973734","doi":"https://doi.org/10.48550/arxiv.2603.09326"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.09326","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09326","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.2603.09326","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057294035","display_name":"Tengjin Weng","orcid":"https://orcid.org/0009-0006-9572-2576"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Weng, Tengjin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128713780","display_name":"Wenhao Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Wenhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128740391","display_name":"Jingyi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128697635","display_name":"Ming Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128740922","display_name":"Lin Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128716970","display_name":"Zhong Ming","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ming, Zhong","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":0.9283999800682068,"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":0.9283999800682068,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.014399999752640724,"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":0.008799999952316284,"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/sensitivity","display_name":"Sensitivity (control systems)","score":0.5422000288963318},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.517300009727478},{"id":"https://openalex.org/keywords/visual-language","display_name":"Visual language","score":0.48089998960494995},{"id":"https://openalex.org/keywords/visual-perception","display_name":"Visual perception","score":0.4643000066280365},{"id":"https://openalex.org/keywords/human-visual-system-model","display_name":"Human visual system model","score":0.44600000977516174},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.4302000105381012},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.421999990940094},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4113999903202057}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6726999878883362},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6046000123023987},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5422000288963318},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.517300009727478},{"id":"https://openalex.org/C2780878386","wikidata":"https://www.wikidata.org/wiki/Q1659648","display_name":"Visual language","level":2,"score":0.48089998960494995},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.4643000066280365},{"id":"https://openalex.org/C160086991","wikidata":"https://www.wikidata.org/wiki/Q5939193","display_name":"Human visual system model","level":3,"score":0.44600000977516174},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4302000105381012},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.421999990940094},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41429999470710754},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4113999903202057},{"id":"https://openalex.org/C2779321571","wikidata":"https://www.wikidata.org/wiki/Q7936605","display_name":"Visual learning","level":2,"score":0.4034000039100647},{"id":"https://openalex.org/C22033958","wikidata":"https://www.wikidata.org/wiki/Q7167036","display_name":"Perceptual learning","level":3,"score":0.36880001425743103},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3359000086784363},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3093000054359436},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3043000102043152},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C74672266","wikidata":"https://www.wikidata.org/wiki/Q815859","display_name":"Language acquisition","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.26019999384880066},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.250900000333786},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2508000135421753},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.09326","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09326","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.2603.09326","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.09326","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/10","display_name":"Reduced inequalities","score":0.5385291576385498}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"large":[1],"language":[2,15],"models":[3],"(MLLMs)":[4],"have":[5],"achieved":[6],"remarkable":[7],"performance":[8],"across":[9],"a":[10,42,60,114],"wide":[11],"range":[12],"of":[13,51,130],"vision":[14],"tasks.":[16],"However,":[17],"their":[18],"ability":[19],"in":[20,25,106,168],"low-level":[21],"visual":[22,28,48,71,107,148,165],"perception,":[23],"particularly":[24],"detecting":[26],"fine-grained":[27,147],"discrepancies,":[29],"remains":[30],"underexplored":[31],"and":[32,92,94,99,122,133,154,164,172],"lacks":[33],"systematic":[34],"analysis.":[35],"In":[36],"this":[37],"work,":[38],"we":[39],"introduce":[40],"OddGridBench,":[41],"controllable":[43],"benchmark":[44],"for":[45,160],"evaluating":[46],"the":[47,128,139,145,158],"discrepancy":[49,108,166],"sensitivity":[50,167],"MLLMs.":[52],"OddGridBench":[53,153],"comprises":[54],"over":[55],"1,400":[56],"grid-based":[57],"images,":[58],"where":[59],"single":[61],"element":[62],"differs":[63],"from":[64],"all":[65,83],"others":[66],"by":[67],"one":[68],"or":[69,78],"multiple":[70],"attributes":[72],"such":[73,89],"as":[74,90],"color,":[75],"size,":[76],"rotation,":[77],"position.":[79],"Experiments":[80],"reveal":[81],"that":[82,118],"evaluated":[84],"MLLMs,":[85],"including":[86],"open-source":[87],"families":[88],"Qwen3-VL":[91],"InternVL3.5,":[93],"proprietary":[95],"systems":[96],"like":[97],"Gemini-2.5-Pro":[98],"GPT-5,":[100],"perform":[101],"far":[102],"below":[103],"human":[104],"levels":[105],"detection.":[109],"We":[110,151],"further":[111],"propose":[112],"OddGrid-GRPO,":[113],"reinforcement":[115],"learning":[116,121],"framework":[117],"integrates":[119],"curriculum":[120],"distance-aware":[123],"reward.":[124],"By":[125],"progressively":[126],"controlling":[127],"difficulty":[129],"training":[131],"samples":[132],"incorporating":[134],"spatial":[135],"proximity":[136],"constraints":[137],"into":[138],"reward":[140],"design,":[141],"OddGrid-GRPO":[142,155],"significantly":[143],"enhances":[144],"model's":[146],"discrimination":[149],"ability.":[150],"hope":[152],"will":[156],"lay":[157],"groundwork":[159],"advancing":[161],"perceptual":[162],"grounding":[163],"multimodal":[169],"intelligence.":[170],"Code":[171],"dataset":[173],"are":[174],"available":[175],"at":[176],"https://wwwtttjjj.github.io/OddGridBench/.":[177]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-12T00:00:00"}
