{"id":"https://openalex.org/W7154516625","doi":"https://doi.org/10.48550/arxiv.2604.12820","title":"RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair","display_name":"RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154516625","doi":"https://doi.org/10.48550/arxiv.2604.12820"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.12820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12820","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","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.2604.12820","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133705444","display_name":"Jagadeesh Rachapudi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rachapudi, Jagadeesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133692291","display_name":"Pranav Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singh, Pranav","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133713729","display_name":"Ritali Vatsi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vatsi, Ritali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091700001","display_name":"Praful Hambarde","orcid":"https://orcid.org/0000-0002-7976-9095"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hambarde, Praful","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133568685","display_name":"Amit Shukla","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shukla, Amit","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":0.2517000138759613,"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":0.2517000138759613,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.19140000641345978,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11147","display_name":"Misinformation and Its Impacts","score":0.10119999945163727,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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/inference","display_name":"Inference","score":0.4553000032901764},{"id":"https://openalex.org/keywords/retraining","display_name":"Retraining","score":0.3939000070095062},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.3422999978065491},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.3402000069618225},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.33869999647140503},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.33149999380111694},{"id":"https://openalex.org/keywords/frass","display_name":"Frass","score":0.32749998569488525},{"id":"https://openalex.org/keywords/misinformation","display_name":"Misinformation","score":0.3264000117778778}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7785000205039978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5101000070571899},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.49799999594688416},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4553000032901764},{"id":"https://openalex.org/C2778712577","wikidata":"https://www.wikidata.org/wiki/Q3505966","display_name":"Retraining","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.3422999978065491},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C109982019","wikidata":"https://www.wikidata.org/wiki/Q2098762","display_name":"Frass","level":3,"score":0.32749998569488525},{"id":"https://openalex.org/C2776990098","wikidata":"https://www.wikidata.org/wiki/Q13579947","display_name":"Misinformation","level":2,"score":0.3264000117778778},{"id":"https://openalex.org/C2777402240","wikidata":"https://www.wikidata.org/wiki/Q6783436","display_name":"Masking (illustration)","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.3025999963283539},{"id":"https://openalex.org/C122783720","wikidata":"https://www.wikidata.org/wiki/Q183065","display_name":"Interpreter","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C115168132","wikidata":"https://www.wikidata.org/wiki/Q55813","display_name":"Machine code","level":3,"score":0.2842000126838684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2840000092983246},{"id":"https://openalex.org/C71745522","wikidata":"https://www.wikidata.org/wiki/Q2476929","display_name":"Confidentiality","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C2779623668","wikidata":"https://www.wikidata.org/wiki/Q7652842","display_name":"SwIPe","level":2,"score":0.2662999927997589}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.12820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12820","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.12820","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.12820","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.4969005882740021,"id":"https://metadata.un.org/sdg/16","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":[1,80],"models":[2],"(LLMs)":[3],"inherently":[4],"absorb":[5],"harmful":[6,182],"knowledge,":[7,241],"misinformation,":[8],"and":[9,42,112,187,227,236],"personal":[10,188],"data":[11,189],"during":[12],"pretraining":[13],"on":[14],"large-scale":[15],"web":[16],"corpora,":[17],"with":[18,133,171,242],"no":[19],"native":[20],"mechanism":[21],"for":[22,100,108,230],"selective":[23],"removal.":[24],"While":[25],"machine":[26],"unlearning":[27,101,139,170],"offers":[28],"a":[29,65,90,97,105,114,136,146],"principled":[30],"solution,":[31],"existing":[32],"approaches":[33],"are":[34,119],"provider-centric,":[35],"requiring":[36],"retraining":[37],"pipelines,":[38],"curated":[39],"retain":[40],"datasets,":[41],"direct":[43],"intervention":[44],"by":[45],"model":[46,92,99,107,116,206,232],"service":[47],"providers":[48],"(MSPs),":[49],"thereby":[50],"excluding":[51],"end":[52],"users":[53,70],"from":[54,159],"controlling":[55],"their":[56],"own":[57],"data.":[58],"We":[59],"introduce":[60],"Interactive":[61],"Machine":[62],"Unlearning":[63],"(IMU),":[64],"new":[66],"paradigm":[67],"in":[68],"which":[69],"can":[71],"instruct":[72],"LLMs":[73],"to":[74,161,173,210,214,245],"forget":[75,196],"targeted":[76],"knowledge":[77,183],"through":[78],"natural":[79],"at":[81],"inference":[82],"time.":[83],"To":[84],"realize":[85],"IMU,":[86],"we":[87,127],"propose":[88],"RePAIR,":[89,126],"prompt-aware":[91],"repair":[93,110],"framework":[94,229],"comprising":[95],"(i)":[96],"watchdog":[98],"intent":[102],"detection,":[103],"(ii)":[104],"surgeon":[106],"generating":[109],"procedures,":[111],"(iii)":[113],"patient":[115],"whose":[117],"parameters":[118],"updated":[120],"autonomously.":[121],"At":[122],"the":[123],"core":[124],"of":[125],"develop":[128],"Steering":[129],"Through":[130],"Activation":[131],"Manipulation":[132],"PseudoInverse":[134],"(STAMP),":[135],"training-free,":[137],"single-sample":[138],"method":[140],"that":[141,192],"redirects":[142],"MLP":[143],"activations":[144],"toward":[145],"refusal":[147],"subspace":[148],"via":[149],"closed-form":[150],"pseudoinverse":[151],"updates.":[152],"Its":[153],"low-rank":[154],"variant":[155],"reduces":[156],"computational":[157],"complexity":[158],"O(d^3)":[160],"O(r^3":[162],"+":[163],"r^2":[164],"*":[165],"d),":[166],"enabling":[167],"efficient":[168],"on-device":[169,237],"up":[172,209,213],"~3x":[174],"speedup":[175],"over":[176,239],"training-based":[177],"baselines.":[178,219],"Extensive":[179],"experiments":[180],"across":[181],"suppression,":[184],"misinformation":[185],"correction,":[186],"erasure":[190],"demonstrate":[191],"RePAIR":[193,223],"achieves":[194],"near-zero":[195],"scores":[197],"(Acc_f":[198],"=":[199,202],"0.00,":[200],"F-RL":[201],"0.00)":[203],"while":[204],"preserving":[205],"utility":[207],"(Acc_r":[208],"84.47,":[211],"R-RL":[212],"0.88),":[215],"outperforming":[216],"six":[217],"state-of-the-art":[218],"These":[220],"results":[221],"establish":[222],"as":[224],"an":[225],"effective":[226],"practical":[228],"user-driven":[231],"editing,":[233],"advancing":[234],"transparent":[235],"control":[238],"learned":[240],"potential":[243],"extensions":[244],"multimodal":[246],"foundation":[247],"models.":[248]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-16T00:00:00"}