{"id":"https://openalex.org/W7161686345","doi":"https://doi.org/10.48550/arxiv.2605.18313","title":"Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering","display_name":"Wasserstein Equilibrium Decoding for Reliable Medical Visual Question Answering","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161686345","doi":"https://doi.org/10.48550/arxiv.2605.18313"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18313","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18313","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.2605.18313","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136468746","display_name":"Luca Hagen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hagen, Luca","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136488154","display_name":"Johanna P. M\u00fcller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M\u00fcller, Johanna P.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136485400","display_name":"Weitong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Weitong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042310701","display_name":"Mengyun Qiao","orcid":"https://orcid.org/0000-0002-5157-1079"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiao, Mengyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136470710","display_name":"Bernhard Kainz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kainz, Bernhard","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":0.916700005531311,"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":0.916700005531311,"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":0.03269999846816063,"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":0.01769999973475933,"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/discriminative-model","display_name":"Discriminative model","score":0.6775000095367432},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5911999940872192},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5870000123977661},{"id":"https://openalex.org/keywords/greedy-algorithm","display_name":"Greedy algorithm","score":0.5511999726295471},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5430999994277954},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5131999850273132},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.3783999979496002}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6775000095367432},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5911999940872192},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5870000123977661},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5566999912261963},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.5511999726295471},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5430999994277954},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5131999850273132},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44209998846054077},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38769999146461487},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.3783999979496002},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.335999995470047},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3246000111103058},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3018999993801117},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2957000136375427},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29170000553131104},{"id":"https://openalex.org/C159254197","wikidata":"https://www.wikidata.org/wiki/Q1144915","display_name":"Lexicographical order","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C152948882","wikidata":"https://www.wikidata.org/wiki/Q4060686","display_name":"Belief propagation","level":3,"score":0.27469998598098755},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18313","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18313","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.2605.18313","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18313","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":[{"score":0.679530918598175,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Small":[0],"vision-language":[1,47],"models":[2,48],"(2-8B)":[3],"are":[4],"well-suited":[5],"for":[6,49],"clinical":[7],"deployment":[8],"due":[9],"to":[10,41,46],"privacy":[11],"constraints,":[12],"limited":[13,25],"connectivity,":[14],"and":[15,76,88,98],"low-latency":[16],"requirements":[17],"favouring":[18],"on-device":[19],"or":[20],"on-premise":[21],"inference.":[22],"However,":[23],"their":[24],"capacity":[26],"exacerbates":[27],"the":[28,114,144,159],"generation":[29],"of":[30],"plausible":[31],"but":[32],"incorrect":[33],"outputs.":[34],"We":[35,53],"extend":[36],"game-theoretic":[37,160],"decoding,":[38],"previously":[39],"restricted":[40],"text-only,":[42],"closed-ended":[43],"NLP":[44],"tasks,":[45],"open-ended":[50],"Medical":[51],"VQA.":[52],"introduce":[54],"a":[55],"semantically":[56],"aware":[57],"Wasserstein":[58,145],"stopping":[59],"criterion":[60,146],"that":[61],"replaces":[62],"lexical":[63],"order":[64],"matching,":[65],"enabling":[66],"convergence":[67,149],"based":[68],"on":[69],"semantic":[70],"consensus":[71],"among":[72],"near-synonymous":[73],"candidate":[74],"answers":[75],"avoiding":[77],"unnecessary":[78],"iterations":[79,150],"caused":[80],"by":[81,106,151],"clinically":[82],"equivalent":[83],"ranking":[84],"swaps.":[85],"On":[86,101,124],"VQA-RAD":[87],"PathVQA,":[89,125],"we":[90,103],"obtain":[91],"consistent,":[92],"statistically":[93],"significant":[94],"improvements":[95],"over":[96],"greedy":[97,115,132],"discriminative":[99],"baselines.":[100],"VQA-RAD,":[102],"improve":[104],"Qwen3-VL-2B":[105],"+3.5":[107],"percentage":[108],"points":[109],"(p":[110],"&lt;":[111],"0.01),":[112],"surpassing":[113],"4B":[116],"model,":[117],"with":[118,127,141],"similar":[119],"trends":[120],"at":[121,166],"larger":[122],"scales.":[123],"Gemma-3-4B":[126],"BDG":[128],"matches":[129],"MedGemma-4B":[130],"under":[131],"decoding":[133],"despite":[134],"no":[135],"domain-specific":[136],"fine-tuning.":[137],"At":[138],"accuracy":[139],"parity":[140],"classic":[142],"BDG,":[143],"reduces":[147],"average":[148],"approximately":[152],"20%,":[153],"improving":[154],"inference":[155],"efficiency":[156],"while":[157],"preserving":[158],"equilibrium":[161],"behaviour.":[162],"Code":[163],"is":[164],"available":[165],"https://github.com/luca-hagen/":[167],"Wasserstein-BDG-medical-VQA.":[168]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}
