{"id":"https://openalex.org/W7165416254","doi":"https://doi.org/10.48550/arxiv.2606.20280","title":"ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval","display_name":"ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval","publication_year":2026,"publication_date":"2026-06-18","ids":{"openalex":"https://openalex.org/W7165416254","doi":"https://doi.org/10.48550/arxiv.2606.20280"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.20280","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20280","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.20280","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138973739","display_name":"Yuhan Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yuhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138963662","display_name":"Pei Fu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Pei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138982203","display_name":"Hang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Hang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101318495","display_name":"Yukun Qi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Yukun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139003145","display_name":"Chao Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Chao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110389262","display_name":"Fu J","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fu, Jingwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138998632","display_name":"Zhen Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Zhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139021474","display_name":"Bin Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Bin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139021307","display_name":"Zhenbo Luo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Zhenbo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139018694","display_name":"Jian Luan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luan, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5031227866","display_name":"Jingmin Xin","orcid":"https://orcid.org/0000-0003-1906-2327"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xin, Jingmin","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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9146000146865845,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.9146000146865845,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.01269999984651804,"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":0.009600000455975533,"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/ranking","display_name":"Ranking (information retrieval)","score":0.6517999768257141},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5127000212669373},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49380001425743103},{"id":"https://openalex.org/keywords/learning-to-rank","display_name":"Learning to rank","score":0.39890000224113464},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.39480000734329224},{"id":"https://openalex.org/keywords/closed-captioning","display_name":"Closed captioning","score":0.33079999685287476},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.3260999917984009}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7487999796867371},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6517999768257141},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5278000235557556},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5127000212669373},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49380001425743103},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4083999991416931},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.39890000224113464},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.39480000734329224},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35600000619888306},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.33079999685287476},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.3260999917984009},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.30820000171661377},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.2858000099658966},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.25769999623298645},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.20280","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20280","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2606.20280","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.20280","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Leveraging":[0],"Multimodal":[1,20],"Large":[2],"Language":[3],"Models":[4],"(MLLMs)":[5],"via":[6],"contrastive":[7,34,67],"learning":[8,68],"has":[9],"become":[10],"a":[11,72,121,128,203],"mainstream":[12],"paradigm":[13,35],"for":[14,59,208],"improving":[15],"the":[16,28,33,43,46,54,78,102,106,159,179,186],"performance":[17],"of":[18,45,142,181],"Universal":[19],"Retrieval":[21],"(UMR).":[22],"However,":[23],"previous":[24],"works":[25],"have":[26],"ignored":[27],"grain":[29,111,135,197,233],"blindness":[30,40,136],"when":[31],"adapting":[32],"into":[36],"retrieval":[37,156,218],"tasks.":[38],"Grain":[39],"refers":[41],"to":[42,48,98,101,108,155,161],"tendency":[44],"model":[47,107,160],"overlook":[49],"grain-level":[50],"information":[51,80,112],"contained":[52],"in":[53,231],"query,":[55],"which":[56],"is":[57],"crucial":[58],"effectively":[60],"handling":[61],"complex":[62],"queries.":[63],"This":[64],"stems":[65],"from":[66,113],"treating":[69],"samples":[70,183],"as":[71],"binary":[73],"classification":[74],"(positive/negative),":[75],"while":[76,184],"ignoring":[77],"different":[79],"carried":[81],"by":[82],"each":[83,114],"negative":[84,182],"sample.":[85],"To":[86,193],"address":[87],"this,":[88],"we":[89,119,147,199],"argue":[90],"that":[91,133],"negatives":[92],"should":[93],"be":[94],"treated":[95],"differently":[96],"according":[97],"their":[99],"similarity":[100,187],"positive":[103,190],"sample,":[104],"enabling":[105],"learn":[109],"distinct":[110],"negative.":[115,192],"In":[116],"this":[117],"paper,":[118],"introduce":[120,201],"simple":[122],"but":[123],"effective":[124],"framework,":[125],"called":[126],"ELVA,":[127],"novel":[129],"rule-based":[130,173],"RL":[131],"framework":[132],"mitigates":[134],"through":[137],"ranking-driven":[138],"MLLMs.":[139],"1)":[140],"Instead":[141],"relying":[143],"on":[144,225],"reward":[145],"models,":[146],"extend":[148],"Reinforcement":[149],"Learning":[150],"with":[151],"Verifiable":[152],"Rewards":[153],"(RLVR)":[154],"tasks,":[157],"allowing":[158],"explore":[162],"new":[163,204],"ranking":[164,168,180],"behaviors":[165],"without":[166],"explicit":[167],"labels.":[169],"2)":[170],"By":[171],"utilizing":[172],"rewards,":[174],"our":[175],"approach":[176],"jointly":[177],"optimizes":[178],"enlarging":[185],"gap":[188],"between":[189],"and":[191,220],"more":[194],"precisely":[195],"measure":[196],"blindness,":[198],"further":[200,227],"MRBench,":[202],"benchmark":[205],"specifically":[206],"designed":[207],"multi-grain":[209],"query":[210],"scenarios.":[211],"ELVA":[212],"achieves":[213],"state-of-the-art":[214],"results":[215],"across":[216],"standard":[217],"benchmarks,":[219],"its":[221,229],"notable":[222],"13.1%":[223],"improvement":[224],"MRBench":[226],"demonstrates":[228],"effectiveness":[230],"alleviating":[232],"blindness.":[234]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-20T00:00:00"}