{"id":"https://openalex.org/W7166808323","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1643","title":"ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG","display_name":"ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7166808323","doi":"https://doi.org/10.18653/v1/2026.findings-acl.1643"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-acl.1643","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1643","pdf_url":"https://aclanthology.org/2026.findings-acl.1643.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-acl.1643.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065706865","display_name":"Xianming Hu","orcid":"https://orcid.org/0000-0003-4655-7632"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xianming Hu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109731818","display_name":"Jingyang Chen","orcid":"https://orcid.org/0009-0004-6945-8260"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jingyang Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139760451","display_name":"Bin Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bin Tang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139723546","display_name":"Yihe Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yihe Liu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139729288","display_name":"Yihong Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yihong Huang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139816924","display_name":"Hongbo Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongbo Zhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113377586","display_name":"Nuoyi Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nuoyi Chen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139805386","display_name":"Jie M. Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jie Zhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139817613","display_name":"Ping Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ping Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139787780","display_name":"Kai Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kai Zhang","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":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.79832961,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"32840","last_page":"32859"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.36629998683929443,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.36629998683929443,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.13779999315738678,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.11699999868869781,"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/zoom","display_name":"Zoom","score":0.39419999718666077},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.32710000872612},{"id":"https://openalex.org/keywords/information-system","display_name":"Information system","score":0.26460000872612},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.26159998774528503}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6025999784469604},{"id":"https://openalex.org/C124913957","wikidata":"https://www.wikidata.org/wiki/Q1232548","display_name":"Zoom","level":3,"score":0.39419999718666077},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3822000026702881},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36090001463890076},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.32710000872612},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2906999886035919},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27250000834465027},{"id":"https://openalex.org/C180198813","wikidata":"https://www.wikidata.org/wiki/Q121182","display_name":"Information system","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.24809999763965607}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-acl.1643","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1643","pdf_url":"https://aclanthology.org/2026.findings-acl.1643.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-acl.1643","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-acl.1643","pdf_url":"https://aclanthology.org/2026.findings-acl.1643.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: ACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6304150607","display_name":null,"funder_award_id":"62276099","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7166808323.pdf","grobid_xml":"https://content.openalex.org/works/W7166808323.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Retrieval-Augmented":[0],"Generation":[1],"is":[2,10],"a":[3,30,56,62,69,95],"powerful":[4],"tool":[5],"for":[6,104],"NLP":[7],"applications.Yet,":[8],"it":[9,54,80],"challenging":[11],"to":[12,36,66,86,99,128],"encode":[13],"large":[14],"knowledge":[15,152],"bases":[16],"as":[17,44,173,175],"compact":[18],"offline":[19,110],"structures":[20,125],"while":[21,133,154],"simultaneously":[22],"achieving":[23,155],"accurate,":[24],"low-latency":[25],"online":[26,115,169],"retrieval.We":[27],"propose":[28],"ZoomRAG,":[29],"coarse-to-fine,":[31],"hierarchical":[32],"graph":[33,59],"inference":[34],"method":[35],"tackle":[37],"the":[38,41,51,74,77,83,138],"challenges.ZoomRAG":[39],"formulates":[40],"retrieval":[42,170],"task":[43],"random":[45,64,97],"walks":[46],"across":[47],"multi-scale":[48,123],"relational":[49,58,124],"graphs.At":[50],"coarse":[52],"level,":[53,79],"constructs":[55],"global":[57],"and":[60,90,93,113],"performs":[61],"query-initiated":[63],"walk":[65,98],"quickly":[67],"locate":[68],"few":[70],"relevant":[71],"documents":[72,85],"over":[73,121,162],"entire":[75],"corpus.At":[76],"finer":[78],"\"zooms":[81],"into\"":[82],"selected":[84],"capture":[87],"fine-grained":[88],"semantic":[89],"temporal":[91],"relations,":[92],"conducts":[94],"second":[96],"pinpoint":[100],"salient":[101],"evidence":[102,132],"chunks":[103],"generation.This":[105],"coarse-to-fine":[106],"strategy":[107],"substantially":[108],"reduces":[109],"indexing":[111],"costs":[112],"accelerates":[114],"retrieval.Moreover,":[116],"random-walk":[117],"based":[118],"topological":[119],"reasoning":[120],"rich,":[122],"enables":[126],"ZoomRAG":[127,148],"effectively":[129],"aggregate":[130],"multi-hop":[131],"suppressing":[134],"noise.Finally,":[135],"we":[136],"address":[137],"difficulty":[139],"of":[140,181],"handling":[141],"concurrent":[142],"RAG":[143,164],"queries":[144,182],"by":[145,178],"algorithm-parallel":[146],"ZoomRAG.Overall,":[147],"avoids":[149],"building":[150],"expensive":[151],"graphs":[153],"2.2%":[156],"-4.9%":[157],"absolute":[158],"gains":[159],"in":[160],"accuracy":[161],"SOTA":[163],"models,":[165],"with":[166],"an":[167],"average":[168],"latency":[171],"per-query":[172],"low":[174],"0.019":[176],"secs":[177],"processing":[179],"hundreds":[180],"concurrently.":[183]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-07-02T00:00:00"}
