{"id":"https://openalex.org/W7161005726","doi":"https://doi.org/10.48550/arxiv.2605.11685","title":"Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter","display_name":"Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161005726","doi":"https://doi.org/10.48550/arxiv.2605.11685"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.11685","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11685","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.2605.11685","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136010830","display_name":"Zeguan Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Zeguan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136022547","display_name":"Xuanzhe Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Xuanzhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136079771","display_name":"Yun Yuan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136060706","display_name":"Yong Wang","orcid":"https://orcid.org/0000-0001-8473-5386"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136061966","display_name":"Jian Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Jian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069858596","display_name":"Yanqing Hu","orcid":"https://orcid.org/0000-0003-0653-0259"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Yanqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136005717","display_name":"Guanhua Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Guanhua","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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.8112999796867371,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.8112999796867371,"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":0.02630000002682209,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.023600000888109207,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/minor","display_name":"Minor (academic)","score":0.7878999710083008},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5277000069618225},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5189999938011169},{"id":"https://openalex.org/keywords/fragility","display_name":"Fragility","score":0.4672999978065491},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.3467999994754791}],"concepts":[{"id":"https://openalex.org/C2779760435","wikidata":"https://www.wikidata.org/wiki/Q5396169","display_name":"Minor (academic)","level":2,"score":0.7878999710083008},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5799999833106995},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5277000069618225},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5189999938011169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5099999904632568},{"id":"https://openalex.org/C80191262","wikidata":"https://www.wikidata.org/wiki/Q5477668","display_name":"Fragility","level":2,"score":0.4672999978065491},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.3467999994754791},{"id":"https://openalex.org/C180010258","wikidata":"https://www.wikidata.org/wiki/Q1439151","display_name":"Major and minor","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.27379998564720154},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.24709999561309814}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.11685","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11685","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.2605.11685","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.11685","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":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8014140129089355}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"language":[1],"model":[2,13],"(LLM)":[3],"unlearning":[4,69,135,146],"aims":[5],"to":[6,104,159],"remove":[7],"specific":[8],"data":[9],"influences":[10],"from":[11,60,117],"pre-trained":[12],"without":[14],"costly":[15],"retraining,":[16],"addressing":[17],"privacy,":[18],"copyright,":[19],"and":[20],"safety":[21],"concerns.":[22],"However,":[23],"recent":[24],"studies":[25],"reveal":[26],"a":[27,61,110,133],"critical":[28],"vulnerability:":[29],"unlearned":[30],"models":[31],"rapidly":[32],"recover":[33],"\"forgotten\"":[34],"knowledge":[35,96],"through":[36],"relearning":[37,83,160],"attacks.":[38,161],"This":[39],"fragility":[40,59],"raises":[41],"serious":[42],"security":[43],"concerns,":[44],"especially":[45],"for":[46],"open-weight":[47],"models.":[48],"In":[49],"this":[50,58,125],"work,":[51],"we":[52,127],"investigate":[53],"the":[54,85,118],"fundamental":[55],"mechanism":[56],"underlying":[57],"representation":[62],"geometry":[63],"perspective.":[64],"We":[65,107],"discover":[66],"that":[67,113,137],"existing":[68],"methods":[70,175],"predominantly":[71],"optimize":[72],"along":[73],"dominant":[74,89],"components,":[75],"leaving":[76],"minor":[77,99,140],"components":[78,90,100,141],"largely":[79],"unchanged.":[80],"Critically,":[81],"during":[82],"attacks,":[84],"modifications":[86],"in":[87,142,148],"these":[88,149],"are":[91],"easily":[92],"reversed,":[93],"enabling":[94],"rapid":[95],"recovery,":[97],"whereas":[98],"exhibit":[101],"stronger":[102],"resistance":[103,158],"such":[105],"reversal.":[106],"further":[108],"provide":[109],"theoretical":[111],"analysis":[112],"explains":[114],"both":[115],"observations":[116],"spectral":[119],"structure":[120],"of":[121],"representations.":[122,143],"Building":[123],"on":[124,164],"insight,":[126],"propose":[128],"Minor":[129],"Component":[130],"Unlearning":[131],"(MCU),":[132],"novel":[134],"approach":[136],"explicitly":[138],"targets":[139],"By":[144],"concentrating":[145],"effects":[147],"inherently":[150],"robust":[151],"directions,":[152],"our":[153,168],"method":[154],"achieves":[155],"substantially":[156],"improved":[157],"Extensive":[162],"experiments":[163],"three":[165],"datasets":[166],"validate":[167],"approach,":[169],"demonstrating":[170],"significant":[171],"improvements":[172],"over":[173],"state-of-the-art":[174],"including":[176],"sharpness-aware":[177],"minimization.":[178]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-14T00:00:00"}