{"id":"https://openalex.org/W7154726195","doi":"https://doi.org/10.48550/arxiv.2604.14172","title":"Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generations","display_name":"Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generations","publication_year":2026,"publication_date":"2026-03-25","ids":{"openalex":"https://openalex.org/W7154726195","doi":"https://doi.org/10.48550/arxiv.2604.14172"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.14172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14172","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.14172","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133847062","display_name":"Ziyin Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Ziyin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133906464","display_name":"Jianyi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jianyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133906683","display_name":"Xu ji","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"ji, Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133882764","display_name":"Yilong Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yilong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102831145","display_name":"Jiameng Han","orcid":"https://orcid.org/0009-0007-3073-2113"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Han, Jiameng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101232158","display_name":"Zhangchi Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Zhangchi","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/T10734","display_name":"Information and Cyber Security","score":0.4221000075340271,"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/T10734","display_name":"Information and Cyber Security","score":0.4221000075340271,"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":0.10819999873638153,"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/T12479","display_name":"Web Application Security Vulnerabilities","score":0.056299999356269836,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/vulnerability","display_name":"Vulnerability (computing)","score":0.588100016117096},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4867999851703644},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.44620001316070557},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.42910000681877136},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.37869998812675476},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.36230000853538513}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6991999745368958},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.588100016117096},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4867999851703644},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.44620001316070557},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.42910000681877136},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4284999966621399},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3896999955177307},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.37869998812675476},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.36230000853538513},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32760000228881836},{"id":"https://openalex.org/C21711469","wikidata":"https://www.wikidata.org/wiki/Q1194317","display_name":"Conflict resolution","level":2,"score":0.32260000705718994},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.2712000012397766},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2662999927997589},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.25600001215934753},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2533999979496002},{"id":"https://openalex.org/C171686336","wikidata":"https://www.wikidata.org/wiki/Q3532085","display_name":"Topic model","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.14172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14172","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.14172","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.14172","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.44424253702163696,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"are":[4],"essential":[5],"for":[6,236],"analyzing":[7],"and":[8,40,61,67,70,95,134,143,198,226],"addressing":[9],"vulnerabilities":[10,17],"in":[11,20,112,158,191,207],"cybersecurity.":[12],"However,":[13],"among":[14],"over":[15],"200,000":[16],"were":[18],"discovered":[19],"the":[21,37,56,79,126,153,176,186,209],"past":[22],"decade,":[23],"more":[24,196],"than":[25],"30,000":[26],"have":[27],"been":[28],"changed":[29],"or":[30],"updated.":[31],"This":[32,72],"necessitates":[33],"frequent":[34],"updates":[35],"to":[36,46,77,87,120,147,168,193,213],"training":[38,84],"datasets":[39],"internal":[41],"knowledge":[42,48,59,81,88,215,224,237],"bases":[43],"of":[44,58,91,155,178,188],"LLMs":[45,192,235],"maintain":[47],"consistency.":[49],"In":[50,217],"this":[51,100],"paper,":[52],"we":[53,102,129,161],"focus":[54],"on":[55,140,152,234],"problem":[57,73],"discrepancy":[60],"conflict":[62],"within":[63],"CVE":[64,156],"(Common":[65],"Vulnerabilities":[66],"Exposures)":[68],"detection":[69],"analysis.":[71],"hinders":[74],"LLMs'":[75,149],"ability":[76],"retrieve":[78],"latest":[80,210],"from":[82,231],"original":[83],"datasets,":[85],"leading":[86],"conflicts,":[89],"fabrications":[90],"factually":[92],"incorrect":[93],"results,":[94],"generation":[96,159],"hallucinations.":[97],"To":[98],"address":[99],"problem,":[101],"propose":[103],"an":[104,135],"innovative":[105],"two-stage":[106],"framework":[107,172,220],"called":[108],"CRVA-TGRAG":[109],"(Conflict":[110],"Resolution":[111],"Vulnerability":[113],"Analysis":[114],"via":[115],"Teacher-Guided":[116],"Retrieval-Augmented":[117],"Generation).":[118],"First,":[119],"improve":[121],"document":[122],"retrieval":[123,127,137,154,180],"accuracy":[124,206],"during":[125],"stage,":[128,160],"utilize":[130],"Parent":[131],"Document":[132],"Segmentation":[133],"ensemble":[136],"scheme":[138],"based":[139,151],"semantic":[141],"similarity":[142],"inverted":[144],"indexing.":[145],"Second,":[146],"enhance":[148],"capabilities":[150],"dataset":[157],"employ":[162],"a":[163],"teacher-guided":[164],"preference":[165,189],"optimization":[166],"technique":[167],"fine-tune":[169],"LLMs.":[170],"Our":[171],"not":[173],"only":[174],"enhances":[175],"quality":[177],"content":[179],"through":[181],"RAG":[182],"but":[183],"also":[184],"leverages":[185],"advantages":[187],"fine-tuning":[190],"answer":[194],"questions":[195],"effectively":[197],"precisely.":[199],"Experiments":[200],"demonstrate":[201],"our":[202,219],"method":[203],"achieves":[204],"higher":[205],"retrieving":[208],"CVEs":[211],"compared":[212],"external":[214],"bases.":[216],"conclusion,":[218],"significantly":[221],"mitigates":[222],"potential":[223],"conflicts":[225],"inconsistencies":[227],"that":[228],"may":[229],"arise":[230],"relying":[232],"solely":[233],"retrieval.":[238]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-18T00:00:00"}