{"id":"https://openalex.org/W7165375984","doi":"https://doi.org/10.48550/arxiv.2606.19960","title":"Stellar: Scalable Multimodal Document Retrieval for Natural Language Queries","display_name":"Stellar: Scalable Multimodal Document Retrieval for Natural Language Queries","publication_year":2026,"publication_date":"2026-06-18","ids":{"openalex":"https://openalex.org/W7165375984","doi":"https://doi.org/10.48550/arxiv.2606.19960"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.19960","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19960","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.2606.19960","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101893660","display_name":"Yi Guo","orcid":"https://orcid.org/0009-0006-1890-6726"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yuxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114402761","display_name":"Zhonghao Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Zhonghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139007558","display_name":"Yuren Mao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mao, Yuren","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138993232","display_name":"Yuhang Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yuhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088891882","display_name":"Congcong Ge","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ge, Congcong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138974324","display_name":"Xiaolu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Xiaolu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138965994","display_name":"Jun Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Jun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139019564","display_name":"Yunjun Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Yunjun","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":0.49059998989105225,"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":0.49059998989105225,"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/T10028","display_name":"Topic Modeling","score":0.19820000231266022,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.12250000238418579,"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/scalability","display_name":"Scalability","score":0.6894000172615051},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5320000052452087},{"id":"https://openalex.org/keywords/document-retrieval","display_name":"Document retrieval","score":0.49380001425743103},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.49300000071525574},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.46790000796318054},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.4456000030040741},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.42089998722076416},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.40070000290870667},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.39559999108314514}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8572999835014343},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6894000172615051},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5320000052452087},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.49380001425743103},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.49300000071525574},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.46790000796318054},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.4456000030040741},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4359999895095825},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.42089998722076416},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.40070000290870667},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.39559999108314514},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.39329999685287476},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3930000066757202},{"id":"https://openalex.org/C177937566","wikidata":"https://www.wikidata.org/wiki/Q4223102","display_name":"Document clustering","level":3,"score":0.3840000033378601},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3747999966144562},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3716999888420105},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3555999994277954},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.34929999709129333},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C551230270","wikidata":"https://www.wikidata.org/wiki/Q4368942","display_name":"Data retrieval","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C2781213101","wikidata":"https://www.wikidata.org/wiki/Q6398558","display_name":"Keyword spotting","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C2474386","wikidata":"https://www.wikidata.org/wiki/Q461183","display_name":"Text corpus","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C82687282","wikidata":"https://www.wikidata.org/wiki/Q66221","display_name":"Auxiliary memory","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.19960","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19960","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.2606.19960","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19960","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":[{"score":0.5073122978210449,"id":"https://metadata.un.org/sdg/1","display_name":"No poverty"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0,109],"document":[1,7,30,72,78,127],"retrieval--selecting":[2],"the":[3,132,159],"most":[4],"relevant":[5],"multimodal":[6,71],"from":[8],"a":[9,14,69,85,108,115,151,166,179],"large":[10],"corpus":[11],"to":[12,41,56,118,129,199],"answer":[13],"natural":[15],"language":[16],"query--plays":[17],"an":[18,143],"essential":[19],"role":[20],"in":[21],"Retrieval-Augmented":[22],"Generation":[23],"(RAG)":[24],"systems.":[25],"State-of-the-art":[26],"methods":[27,201],"represent":[28],"each":[29],"and":[31,37,59,82,125,155,178,190],"query":[32,191],"with":[33],"multiple":[34],"token-level":[35,77],"embeddings":[36,79,90,162],"use":[38],"late":[39,94],"interaction":[40],"achieve":[42],"high":[43],"effectiveness.":[44,205],"However,":[45],"such":[46],"multi-vector":[47],"representations":[48],"incur":[49],"substantial":[50],"memory":[51,92,164,188],"overhead":[52,189],"during":[53],"retrieval,":[54],"leading":[55],"poor":[57],"scalability":[58],"hindering":[60],"real-world":[61,176],"deployment.":[62],"In":[63],"this":[64],"paper,":[65],"we":[66],"present":[67],"Stellar,":[68],"scalable":[70],"retrieval":[73,204],"framework":[74],"that":[75,185],"stores":[76],"on":[80,174],"disk":[81],"loads":[83,157],"only":[84,158],"small":[86],"set":[87],"of":[88,196],"candidate":[89,133],"into":[91,163],"for":[93],"interaction.":[95],"Stellar":[96,186],"comprises":[97],"two":[98],"key":[99],"components:":[100],"(i)":[101],"Lexical":[102],"Representation-based":[103],"Filtering":[104],"(LRF),":[105],"which":[106,141],"fine-tunes":[107],"Large":[110],"Language":[111],"Model":[112],"(MLLM)":[113],"as":[114],"sparse":[116],"encoder":[117],"produce":[119],"high-quality":[120],"lexical":[121],"representations,":[122],"enabling":[123],"efficient":[124],"effective":[126,169],"filtering":[128],"significantly":[130],"reduce":[131],"set;":[134],"(ii)":[135],"Efficient":[136],"Disk-backed":[137],"Late":[138],"Interaction":[139],"(DLI),":[140],"designs":[142],"on-disk":[144],"token":[145,161],"embedding":[146],"storage":[147],"layout":[148],"guided":[149],"by":[150,193],"balanced":[152],"clustering":[153],"algorithm,":[154],"dynamically":[156],"necessary":[160],"using":[165],"simple":[167],"yet":[168],"cost":[170],"model.":[171],"Extensive":[172],"experiments":[173],"four":[175],"benchmarks":[177],"newly":[180],"presented":[181],"large-scale":[182],"dataset":[183],"demonstrate":[184],"reduces":[187],"latency":[192],"1-2":[194],"orders":[195],"magnitude":[197],"compared":[198],"existing":[200],"without":[202],"compromising":[203]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-20T00:00:00"}
