{"id":"https://openalex.org/W7160596017","doi":"https://doi.org/10.48550/arxiv.2605.05668","title":"Large Vision-Language Models Get Lost in Attention","display_name":"Large Vision-Language Models Get Lost in Attention","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160596017","doi":"https://doi.org/10.48550/arxiv.2605.05668"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.05668","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05668","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.2605.05668","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135679008","display_name":"Gongli Xi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xi, Gongli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135680214","display_name":"Ye Tian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Ye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135717639","display_name":"Mengyu Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Mengyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060429045","display_name":"Huahui Yi","orcid":"https://orcid.org/0009-0007-9361-5491"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yi, Huahui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135676365","display_name":"Liang Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Liang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135642935","display_name":"Xiaoshuai Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Xiaoshuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135715806","display_name":"Kun Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135707620","display_name":"Wendong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Wendong","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":0.6223999857902527,"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":0.6223999857902527,"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":0.12060000002384186,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.10029999911785126,"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/redundancy","display_name":"Redundancy (engineering)","score":0.4731000065803528},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4189999997615814},{"id":"https://openalex.org/keywords/information-theory","display_name":"Information theory","score":0.35760000348091125},{"id":"https://openalex.org/keywords/unified-model","display_name":"Unified Model","score":0.3538999855518341},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.3456999957561493},{"id":"https://openalex.org/keywords/operator","display_name":"Operator (biology)","score":0.3303000032901764}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6739000082015991},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.4731000065803528},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46950000524520874},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4189999997615814},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4180000126361847},{"id":"https://openalex.org/C52622258","wikidata":"https://www.wikidata.org/wiki/Q131222","display_name":"Information theory","level":2,"score":0.35760000348091125},{"id":"https://openalex.org/C45493050","wikidata":"https://www.wikidata.org/wiki/Q7884934","display_name":"Unified Model","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3456999957561493},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3409999907016754},{"id":"https://openalex.org/C17020691","wikidata":"https://www.wikidata.org/wiki/Q139677","display_name":"Operator (biology)","level":5,"score":0.3303000032901764},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2939999997615814},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2745000123977661},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C134537474","wikidata":"https://www.wikidata.org/wiki/Q17144832","display_name":"Naturalness","level":2,"score":0.2669000029563904}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.05668","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05668","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.2605.05668","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05668","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":[{"display_name":"Industry, innovation and infrastructure","score":0.6673994660377502,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Despite":[0],"the":[1,7,19,25,74],"rapid":[2],"evolution":[3],"of":[4,10,28,79,133],"training":[5],"paradigms,":[6],"decoder":[8],"backbone":[9],"large":[11],"vision--language":[12],"models":[13],"(LVLMs)":[14],"remains":[15],"fundamentally":[16],"rooted":[17],"in":[18,67,146,156],"residual-connection":[20],"Transformer":[21],"architecture.":[22],"Therefore,":[23],"deciphering":[24],"distinct":[26],"roles":[27],"internal":[29],"modules":[30],"is":[31],"critical":[32],"for":[33],"understanding":[34],"model":[35],"mechanics":[36],"and":[37,70,76,144],"guiding":[38],"architectural":[39],"optimization.":[40],"While":[41],"prior":[42],"statistical":[43],"approaches":[44],"have":[45],"provided":[46],"valuable":[47],"attribution-based":[48],"insights,":[49],"they":[50],"often":[51],"lack":[52],"a":[53,63,87,94,131],"unified":[54,64,84],"theoretical":[55],"basis.":[56],"To":[57],"bridge":[58],"this":[59,83],"gap,":[60],"we":[61],"propose":[62],"framework":[65,85],"grounded":[66],"information":[68],"theory":[69],"geometry":[71],"to":[72,136],"quantify":[73],"geometric":[75],"entropic":[77],"nature":[78],"residual":[80],"updates.":[81],"Applying":[82],"reveals":[86],"fundamental":[88],"functional":[89],"decoupling:":[90],"Attention":[91],"acts":[92],"as":[93,103],"subspace-preserving":[95],"operator":[96],"focused":[97],"on":[98],"reconfiguration,":[99],"whereas":[100],"FFNs":[101],"serve":[102],"subspace-expanding":[104],"operators":[105],"driving":[106],"semantic":[107],"innovation.":[108],"Strikingly,":[109],"further":[110],"experiments":[111],"demonstrate":[112],"that":[113,150],"replacing":[114],"learned":[115],"attention":[116],"weights":[117],"with":[118],"predefined":[119],"values":[120],"(e.g.,":[121],"Gaussian":[122],"noise)":[123],"yields":[124],"comparable":[125],"or":[126],"even":[127],"superior":[128],"performance":[129],"across":[130],"majority":[132],"datasets":[134],"relative":[135],"vanilla":[137],"models.":[138],"These":[139],"results":[140],"expose":[141],"severe":[142],"misallocation":[143],"redundancy":[145],"current":[147],"mechanisms,":[148],"suggesting":[149],"state-of-the-art":[151],"LVLMs":[152],"effectively":[153],"``get":[154],"lost":[155],"attention''":[157],"rather":[158],"than":[159],"efficiently":[160],"leveraging":[161],"visual":[162],"context.":[163]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-09T00:00:00"}