{"id":"https://openalex.org/W7156325762","doi":"https://doi.org/10.48550/arxiv.2604.22239","title":"Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA","display_name":"Navigating Large-Scale Document Collections: MuDABench for Multi-Document Analytical QA","publication_year":2026,"publication_date":"2026-04-24","ids":{"openalex":"https://openalex.org/W7156325762","doi":"https://doi.org/10.48550/arxiv.2604.22239"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.22239","doi":"https://doi.org/10.48550/arxiv.2604.22239","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22239","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,"listed_in":[],"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"},"volume":null,"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.22239","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125199771","display_name":"Zhanli Li","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhanli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119967794","display_name":"Yixuan Cao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cao, Yixuan","raw_affiliation_strings":["State Key Laboratory of AI Safety , Institute of Computing Technology , Chinese Academy of Sciences (CAS) , Beijing 100190 , China","University of Chinese Academy of Sciences , Beijing 100049 , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of AI Safety , Institute of Computing Technology , Chinese Academy of Sciences (CAS) , Beijing 100190 , China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences , Beijing 100049 , China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134686474","display_name":"Lvzhou Luo","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luo, Lvzhou","raw_affiliation_strings":["State Key Laboratory of AI Safety , Institute of Computing Technology , Chinese Academy of Sciences (CAS) , Beijing 100190 , China","University of Chinese Academy of Sciences , Beijing 100049 , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of AI Safety , Institute of Computing Technology , Chinese Academy of Sciences (CAS) , Beijing 100190 , China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences , Beijing 100049 , China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5125117188","display_name":"Ping Luo","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Luo, Ping","raw_affiliation_strings":["State Key Laboratory of AI Safety , Institute of Computing Technology , Chinese Academy of Sciences (CAS) , Beijing 100190 , China","University of Chinese Academy of Sciences , Beijing 100049 , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of AI Safety , Institute of Computing Technology , Chinese Academy of Sciences (CAS) , Beijing 100190 , China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]},{"raw_affiliation_string":"University of Chinese Academy of Sciences , Beijing 100049 , China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"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/T10028","display_name":"Topic Modeling","score":0.9394999742507935,"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"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9394999742507935,"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/T10286","display_name":"Information Retrieval and Search Behavior","score":0.03909999877214432,"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":0.00570000009611249,"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/multi-document-question-answering","display_name":"multi-document question answering","score":0.8090000152587891},{"id":"https://openalex.org/keywords/benchmark-design","display_name":"benchmark design","score":0.7139999866485596},{"id":"https://openalex.org/keywords/remote-supervision","display_name":"distant supervision","score":0.9290000200271606},{"id":"https://openalex.org/keywords/financial-databases","display_name":"financial databases","score":0.5220000147819519},{"id":"https://openalex.org/keywords/retrieval-augmented-generation","display_name":"retrieval-augmented generation","score":0.7120000123977661},{"id":"https://openalex.org/keywords/multi-agent-workflows","display_name":"multi-agent workflows","score":0.7799999713897705},{"id":"https://openalex.org/keywords/information-extraction","display_name":"information extraction","score":0.7319999933242798},{"id":"https://openalex.org/keywords/code-generation","display_name":"code generation","score":0.7680000066757202},{"id":"https://openalex.org/keywords/semi-structured-documents","display_name":"semi-structured documents","score":0.6840000152587891}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8125},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.7111999988555908},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.640500009059906},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.6007000207901001},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5855000019073486},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5601999759674072},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.510699987411499},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.4869999885559082},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.4853000044822693},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.43869999051094055},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4361000061035156},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4081000089645386},{"id":"https://openalex.org/C161156560","wikidata":"https://www.wikidata.org/wiki/Q1638872","display_name":"Document retrieval","level":2,"score":0.3707999885082245},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30640000104904175},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2603999972343445}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.22239","doi":"https://doi.org/10.48550/arxiv.2604.22239","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22239","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,"listed_in":[],"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.2604.22239","doi":"https://doi.org/10.48550/arxiv.2604.22239","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22239","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,"listed_in":[],"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":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"introduces":[2],"the":[3,105],"task":[4],"of":[5],"analytical":[6,21,81],"question":[7],"answering":[8],"over":[9,76],"large,":[10],"semi-structured":[11],"document":[12],"collections.":[13],"We":[14,84],"present":[15],"MuDABench,":[16],"a":[17,48,119,131,152],"benchmark":[18],"for":[19,104],"multi-document":[20,39],"QA,":[22],"where":[23],"questions":[24],"require":[25,44],"extracting":[26],"and":[27,60,70,79,95,138,149,171],"synthesizing":[28],"information":[29,45,168],"across":[30],"numerous":[31],"documents":[32,50,117],"to":[33,157],"perform":[34,123],"quantitative":[35],"analysis.":[36],"Unlike":[37],"existing":[38],"QA":[40,82],"benchmarks":[41],"that":[42,90,110,134],"typically":[43],"from":[46],"only":[47],"few":[49],"with":[51],"limited":[52],"cross-document":[53],"reasoning,":[54],"MuDABench":[55,74,178],"demands":[56],"extensive":[57],"inter-document":[58],"analysis":[59,162],"aggregation.":[61],"Constructed":[62],"via":[63],"distant":[64],"supervision":[65],"by":[66],"leveraging":[67],"document-level":[68],"metadata":[69],"annotated":[71],"financial":[72],"databases,":[73],"comprises":[75],"80,000":[77],"pages":[78],"332":[80],"instances.":[83],"also":[85],"propose":[86,130],"an":[87,100],"evaluation":[88],"protocol":[89],"measures":[91],"final":[92],"answer":[93],"accuracy":[94,170],"uses":[96],"intermediate-fact":[97],"coverage":[98],"as":[99,118],"auxiliary":[101],"diagnostic":[102],"signal":[103],"reasoning":[106],"process.":[107],"Experiments":[108],"reveal":[109],"standard":[111],"RAG":[112],"systems,":[113],"which":[114],"treat":[115],"all":[116],"flat":[120],"retrieval":[121],"pool,":[122],"poorly.":[124],"To":[125],"address":[126],"these":[127],"limitations,":[128],"we":[129],"multi-agent":[132],"workflow":[133],"orchestrates":[135],"planning,":[136],"extraction,":[137],"code":[139],"generation":[140],"modules.":[141],"While":[142],"this":[143],"approach":[144],"substantially":[145],"improves":[146],"both":[147],"process":[148],"outcome":[150],"metrics,":[151],"significant":[153],"gap":[154],"remains":[155],"compared":[156],"human":[158],"expert":[159],"performance.":[160],"Our":[161],"identifies":[163],"two":[164],"primary":[165],"bottlenecks:":[166],"single-document":[167],"extraction":[169],"insufficient":[172],"domain-specific":[173],"knowledge":[174],"in":[175],"current":[176],"systems.":[177],"is":[179],"available":[180],"at":[181],"https://github.com/Zhanli-Li/MuDABench.":[182]},"counts_by_year":[],"updated_date":"2026-10-01T08:54:57.152042","created_date":"2026-04-28T00:00:00"}
