{"id":"https://openalex.org/W7159654191","doi":"https://doi.org/10.48550/arxiv.2604.27724","title":"Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering","display_name":"Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering","publication_year":2026,"publication_date":"2026-04-30","ids":{"openalex":"https://openalex.org/W7159654191","doi":"https://doi.org/10.48550/arxiv.2604.27724"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.27724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27724","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.27724","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134949947","display_name":"Xupeng Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xupeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108651778","display_name":"Binbin Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Binbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104258077","display_name":"Chenqian Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Chenqian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134991157","display_name":"Jiaqi Zhang (412762)","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jiaqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031190010","display_name":"Kewen Wang","orcid":"https://orcid.org/0000-0002-0542-3761"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Kewen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134992929","display_name":"Ran Gong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gong, Ran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045217770","display_name":"Jinhan Zhang","orcid":"https://orcid.org/0000-0002-4511-8358"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jinhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5109799804","display_name":"Chihang Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chihang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"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":0.7531999945640564,"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":0.7531999945640564,"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":0.1574999988079071,"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/T13083","display_name":"Advanced Text Analysis Techniques","score":0.009600000455975533,"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/pipeline","display_name":"Pipeline (software)","score":0.6047999858856201},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.5293999910354614},{"id":"https://openalex.org/keywords/centroid","display_name":"Centroid","score":0.47510001063346863},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.4510999917984009},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.3598000109195709},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.34290000796318054},{"id":"https://openalex.org/keywords/iterative-refinement","display_name":"Iterative refinement","score":0.3328999876976013},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.33059999346733093}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7930999994277954},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.619700014591217},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6047999858856201},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5293999910354614},{"id":"https://openalex.org/C146599234","wikidata":"https://www.wikidata.org/wiki/Q511093","display_name":"Centroid","level":2,"score":0.47510001063346863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4715000092983246},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.4510999917984009},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.3598000109195709},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.34290000796318054},{"id":"https://openalex.org/C2779982483","wikidata":"https://www.wikidata.org/wiki/Q6094420","display_name":"Iterative refinement","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.3260999917984009},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.30570000410079956},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2935999929904938},{"id":"https://openalex.org/C143587482","wikidata":"https://www.wikidata.org/wiki/Q1543216","display_name":"Iterative and incremental development","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28780001401901245},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.26910001039505005},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2535000145435333}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.27724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27724","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.27724","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.27724","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.5474704504013062,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Medical":[0],"retrieval-augmented":[1],"generation":[2],"(RAG)":[3],"systems":[4],"typically":[5],"operate":[6],"on":[7,87,127],"text":[8],"chunks":[9],"extracted":[10],"from":[11,188,194,198],"biomedical":[12],"literature,":[13],"discarding":[14],"the":[15,88,121,147,158,174,199],"rich":[16],"visual":[17],"content":[18],"(tables,":[19],"figures,":[20],"structured":[21],"layouts)":[22],"of":[23,45],"original":[24],"document":[25,41],"pages.":[26],"We":[27],"propose":[28],"MED-VRAG,":[29],"an":[30,73],"iterative":[31],"multimodal":[32],"RAG":[33],"framework":[34],"that":[35,176],"retrieves":[36],"and":[37,100,120,196],"reasons":[38],"over":[39,82,157,168],"PMC":[40],"page":[42,53],"images":[43],"instead":[44],"OCR'd":[46],"text.":[47],"The":[48],"system":[49],"pairs":[50],"ColQwen2.5":[51],"patch-level":[52],"embeddings":[54],"with":[55,113,146,173],"a":[56,104,114,153,164,179],"sharded":[57],"MapReduce":[58],"LLM":[59],"filter,":[60],"scaling":[61],"to":[62,109],"~350K":[63],"pages":[64],"while":[65],"keeping":[66],"Stage-1":[67],"retrieval":[68,151],"under":[69],"30":[70],"ms":[71],"via":[72],"offline":[74],"coarse-to-fine":[75],"index":[76],"(C=8":[77],"centroids":[78],"per":[79],"page,":[80],"ANN":[81],"centroids,":[83],"exact":[84],"two-way":[85],"scoring":[86],"top-R":[89],"shortlist).":[90],"A":[91],"vision-language":[92],"model":[93],"(VLM)":[94],"then":[95],"iteratively":[96],"refines":[97],"its":[98],"query":[99],"accumulates":[101],"evidence":[102],"in":[103],"memory":[105,200],"bank":[106],"across":[107],"up":[108],"3":[110],"reasoning":[111],"rounds,":[112],"single":[115],"iteration":[116],"costing":[117],"~15.9":[118],"s":[119,126],"full":[122],"three-round":[123],"pipeline":[124],"~47.8":[125],"4xA100.":[128],"Across":[129],"four":[130],"medical":[131],"QA":[132],"benchmarks":[133],"(MedQA,":[134],"MedMCQA,":[135],"PubMedQA,":[136],"MMLU-Med),":[137],"MEDVRAG":[138],"reaches":[139],"78.6%":[140],"average":[141],"accuracy.":[142],"Under":[143],"controlled":[144],"comparison":[145],"same":[148],"Qwen2.5-VL-32B":[149],"backbone,":[150],"contributes":[152],"+5.8":[154],"point":[155,166],"gain":[156],"no-retrieval":[159],"baseline;":[160],"we":[161],"also":[162],"note":[163],"+1.8":[165],"edge":[167],"MedRAG":[169],"+":[170],"GPT-4":[171],"(76.8%),":[172],"caveat":[175],"this":[177],"is":[178],"cross-paper":[180],"rather":[181],"than":[182],"head-to-head":[183],"comparison.":[184],"Ablations":[185],"isolate":[186],"+1.0":[187,197],"page-image":[189],"vs":[190],"text-chunk":[191],"retrieval,":[192],"+1.5":[193],"iteration,":[195],"bank.":[201]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-02T00:00:00"}
