{"id":"https://openalex.org/W7168158898","doi":"https://doi.org/10.48550/arxiv.2607.09092","title":"AgentKGV: Agentic LLM-RAG Framework with Two-Stage Training for the Fact Verification of Knowledge Graphs","display_name":"AgentKGV: Agentic LLM-RAG Framework with Two-Stage Training for the Fact Verification of Knowledge Graphs","publication_year":2026,"publication_date":"2026-07-10","ids":{"openalex":"https://openalex.org/W7168158898","doi":"https://doi.org/10.48550/arxiv.2607.09092"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.09092","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09092","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.2607.09092","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140515551","display_name":"Yumin Heo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heo, Yumin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045131332","display_name":"Hyeon-gu Lee","orcid":"https://orcid.org/0000-0001-5702-7931"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Hyeon-gu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140534431","display_name":"Sumin Seo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seo, Sumin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140563580","display_name":"Youngjoong Ko","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ko, Youngjoong","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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9520000219345093,"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":0.9520000219345093,"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/T10028","display_name":"Topic Modeling","score":0.010999999940395355,"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/T12292","display_name":"Graph Theory and Algorithms","score":0.008899999782443047,"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/rewriting","display_name":"Rewriting","score":0.5824999809265137},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.491100013256073},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.44449999928474426},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.43970000743865967},{"id":"https://openalex.org/keywords/knowledge-graph","display_name":"Knowledge graph","score":0.43059998750686646},{"id":"https://openalex.org/keywords/routing","display_name":"Routing (electronic design automation)","score":0.37950000166893005}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7494000196456909},{"id":"https://openalex.org/C154690210","wikidata":"https://www.wikidata.org/wiki/Q1668499","display_name":"Rewriting","level":2,"score":0.5824999809265137},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.491100013256073},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.44449999928474426},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43299999833106995},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43230000138282776},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C74172769","wikidata":"https://www.wikidata.org/wiki/Q1446839","display_name":"Routing (electronic design automation)","level":2,"score":0.37950000166893005},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.37279999256134033},{"id":"https://openalex.org/C110251889","wikidata":"https://www.wikidata.org/wiki/Q1569697","display_name":"Model checking","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C2779982483","wikidata":"https://www.wikidata.org/wiki/Q6094420","display_name":"Iterative refinement","level":2,"score":0.3240000009536743},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26489999890327454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.09092","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09092","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.2607.09092","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09092","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":0.4381442070007324,"display_name":"Quality Education","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":{"Knowledge":[0],"graphs":[1],"(KGs)":[2],"are":[3],"often":[4],"automatically":[5],"constructed":[6],"from":[7,88,154],"large-scale":[8],"corpora,":[9],"but":[10],"they":[11],"inevitably":[12],"contain":[13],"factual":[14],"errors":[15],"due":[16],"to":[17,111,156],"noisy":[18],"sources":[19],"and":[20,23,52,69,101,103,136],"extraction":[21],"failures,":[22],"verifying":[24],"them":[25],"reliably":[26],"at":[27,115],"industrial":[28,72],"scale":[29],"remains":[30],"a":[31,77,89,94],"critical":[32],"challenge.":[33],"To":[34,63],"address":[35],"this,":[36],"we":[37,74],"propose":[38],"AgentKGV,":[39],"the":[40,108,118,122,148],"Agentic":[41],"LLM-RAG":[42],"framework":[43,66,127],"for":[44,71,97],"KG":[45],"fact":[46],"Verification,":[47],"that":[48,84,106],"integrates":[49],"dynamic":[50],"routing":[51],"iterative":[53],"query":[54,99],"rewriting,":[55],"which":[56],"handles":[57],"surface-form":[58],"mismatch":[59],"in":[60],"document-level":[61],"retrieval.":[62],"make":[64],"this":[65],"more":[67],"accurate":[68],"cost-efficient":[70],"deployment,":[73],"further":[75,141],"introduce":[76],"two-stage":[78,137],"training":[79,138],"strategy:":[80],"turn-level":[81],"distillation-based":[82],"SFT":[83],"transfers":[85],"reasoning":[86],"ability":[87],"large":[90],"teacher":[91],"model":[92,96],"into":[93],"small":[95],"stable":[98],"rewriting":[100],"reasoning,":[102],"trajectory-level":[104],"GRPO":[105,145],"optimizes":[107],"search":[109,152],"policy":[110],"reduce":[112],"unnecessary":[113],"retrieval":[114],"scale.":[116],"On":[117],"long-tail-predicate":[119],"split":[120],"of":[121,151],"open-domain":[123],"T-REx":[124],"benchmark,":[125],"our":[126],"improves":[128],"macro-F1":[129],"over":[130],"single-turn":[131],"RAG":[132],"by":[133,142],"5.5":[134],"\\%p,":[135],"does":[139],"it":[140],"9.4":[143],"\\%p.":[144],"also":[146],"cuts":[147],"average":[149],"number":[150],"calls":[153],"3.24":[155],"1.63":[157],"without":[158],"lowering":[159],"accuracy.":[160]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-14T00:00:00"}
