{"id":"https://openalex.org/W7124718010","doi":"https://doi.org/10.48550/arxiv.2601.11024","title":"PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation","display_name":"PruneRAG: Confidence-Guided Query Decomposition Trees for Efficient Retrieval-Augmented Generation","publication_year":2026,"publication_date":"2026-01-16","ids":{"openalex":"https://openalex.org/W7124718010","doi":"https://doi.org/10.48550/arxiv.2601.11024"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.11024","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11024","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.2601.11024","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123331245","display_name":"Shuguang Jiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiao, Shuguang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046619214","display_name":"Xinyu Xiao","orcid":"https://orcid.org/0000-0002-8877-8129"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Xinyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123295579","display_name":"Yunfan Wei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Yunfan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123319400","display_name":"Shuhan Qi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Shuhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123342778","display_name":"Chengkai Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Chengkai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123327972","display_name":"Quan Z. Michael Sheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheng, Quan Z. Michael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123291115","display_name":"Lina Yao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yao, Lina","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":0.6636000275611877,"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":0.6636000275611877,"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":0.13819999992847443,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.06289999932050705,"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/query-expansion","display_name":"Query expansion","score":0.6908000111579895},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5455999970436096},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5281000137329102},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5116000175476074},{"id":"https://openalex.org/keywords/query-optimization","display_name":"Query optimization","score":0.4869999885559082},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4828000068664551},{"id":"https://openalex.org/keywords/query-language","display_name":"Query language","score":0.45730000734329224},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.45210000872612},{"id":"https://openalex.org/keywords/sargable","display_name":"Sargable","score":0.40950000286102295}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7204999923706055},{"id":"https://openalex.org/C99016210","wikidata":"https://www.wikidata.org/wiki/Q5488129","display_name":"Query expansion","level":2,"score":0.6908000111579895},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5455999970436096},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5281000137329102},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5116000175476074},{"id":"https://openalex.org/C157692150","wikidata":"https://www.wikidata.org/wiki/Q2919848","display_name":"Query optimization","level":2,"score":0.4869999885559082},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4828000068664551},{"id":"https://openalex.org/C192028432","wikidata":"https://www.wikidata.org/wiki/Q845739","display_name":"Query language","level":2,"score":0.45730000734329224},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.45210000872612},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44780001044273376},{"id":"https://openalex.org/C192939062","wikidata":"https://www.wikidata.org/wiki/Q104840822","display_name":"Sargable","level":4,"score":0.40950000286102295},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.3677000105381012},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.36399999260902405},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3614000082015991},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3384999930858612},{"id":"https://openalex.org/C24028149","wikidata":"https://www.wikidata.org/wiki/Q7094056","display_name":"Online aggregation","level":5,"score":0.3384999930858612},{"id":"https://openalex.org/C24755975","wikidata":"https://www.wikidata.org/wiki/Q4943354","display_name":"Boolean conjunctive query","level":5,"score":0.31869998574256897},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3158000111579895},{"id":"https://openalex.org/C118689300","wikidata":"https://www.wikidata.org/wiki/Q7978614","display_name":"Web query classification","level":4,"score":0.30809998512268066},{"id":"https://openalex.org/C136736807","wikidata":"https://www.wikidata.org/wiki/Q818943","display_name":"Range query (database)","level":5,"score":0.3073999881744385},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C164120249","wikidata":"https://www.wikidata.org/wiki/Q995982","display_name":"Web search query","level":3,"score":0.29739999771118164},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2768000066280365},{"id":"https://openalex.org/C2778258933","wikidata":"https://www.wikidata.org/wiki/Q16918986","display_name":"Decomposition method (queueing theory)","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C2781020372","wikidata":"https://www.wikidata.org/wiki/Q533093","display_name":"On the fly","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.11024","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11024","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.2601.11024","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.11024","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":[{"display_name":"Quality Education","score":0.44434913992881775,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Retrieval-augmented":[0],"generation":[1],"(RAG)":[2],"has":[3],"become":[4],"a":[5,56,71,78,150],"powerful":[6],"framework":[7,75],"for":[8],"enhancing":[9],"large":[10],"language":[11],"models":[12],"in":[13,64],"knowledge-intensive":[14],"and":[15,43,50,61,86,101,109,113,177],"reasoning":[16,20,132],"tasks.":[17],"However,":[18],"as":[19,149],"chains":[21],"deepen":[22],"or":[23],"search":[24],"trees":[25],"expand,":[26],"RAG":[27,66],"systems":[28],"often":[29],"face":[30],"two":[31],"persistent":[32],"failures:":[33],"evidence":[34,62,129,141,157],"forgetting,":[35],"where":[36,155],"retrieved":[37,159],"knowledge":[38],"is":[39,158],"not":[40,161],"effectively":[41],"used,":[42],"inefficiency,":[44],"caused":[45],"by":[46],"uncontrolled":[47],"query":[48,73,80],"expansions":[49],"redundant":[51],"retrieval.":[52],"These":[53],"issues":[54],"reveal":[55],"critical":[57],"gap":[58],"between":[59],"retrieval":[60,115,122,136],"utilization":[63],"current":[65],"architectures.":[67],"We":[68],"propose":[69],"PruneRAG,":[70],"confidence-guided":[72,103],"decomposition":[74,81],"that":[76,97,105,116,172],"builds":[77],"structured":[79],"tree":[82,99],"to":[83,120,152],"perform":[84],"stable":[85],"efficient":[87],"reasoning.":[88],"PruneRAG":[89,173],"introduces":[90],"three":[91],"key":[92],"mechanisms:":[93],"adaptive":[94],"node":[95],"expansion":[96],"regulates":[98],"width":[100],"depth,":[102],"decisions":[104],"accept":[106],"reliable":[107],"answers":[108],"prune":[110],"uncertain":[111],"branches,":[112],"fine-grained":[114],"extracts":[117],"entity-level":[118],"anchors":[119],"improve":[121],"precision.":[123],"Together,":[124],"these":[125],"components":[126],"preserve":[127],"salient":[128],"throughout":[130],"multi-hop":[131,168],"while":[133],"significantly":[134],"reducing":[135],"overhead.":[137],"To":[138],"better":[139],"analyze":[140],"misuse,":[142],"we":[143],"define":[144],"the":[145],"Evidence":[146],"Forgetting":[147],"Rate":[148],"metric":[151],"quantify":[153],"cases":[154],"golden":[156],"but":[160],"correctly":[162],"used.":[163],"Extensive":[164],"experiments":[165],"across":[166],"various":[167],"QA":[169],"benchmarks":[170],"show":[171],"achieves":[174],"superior":[175],"accuracy":[176],"efficiency":[178],"over":[179],"state-of-the-art":[180],"baselines.":[181]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-01-20T00:00:00"}