{"id":"https://openalex.org/W7161584483","doi":"https://doi.org/10.48550/arxiv.2605.15687","title":"ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models","display_name":"ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161584483","doi":"https://doi.org/10.48550/arxiv.2605.15687"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.15687","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15687","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.2605.15687","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136411824","display_name":"Jiahui Guang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guang, Jiahui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136356978","display_name":"Yingjie Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haiyan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136383981","display_name":"Cuiyun Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yingjie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136435771","display_name":"Haiyan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Cuiyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136361287","display_name":"Jing Li","orcid":"https://orcid.org/0009-0008-6520-0704"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101906915","display_name":"Di Shao","orcid":"https://orcid.org/0000-0001-8673-5267"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shao, Di","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136385998","display_name":"Zhaoquan Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Zhaoquan","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.4108000099658966,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.4108000099658966,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.3353999853134155,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.039799999445676804,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hallucinating","display_name":"Hallucinating","score":0.5464000105857849},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5110999941825867},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.46779999136924744},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.43849998712539673},{"id":"https://openalex.org/keywords/usability","display_name":"Usability","score":0.4214000105857849},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.3831000030040741}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7179999947547913},{"id":"https://openalex.org/C2911011789","wikidata":"https://www.wikidata.org/wiki/Q130741","display_name":"Hallucinating","level":2,"score":0.5464000105857849},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5361999869346619},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5110999941825867},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.48339998722076416},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.46779999136924744},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C170130773","wikidata":"https://www.wikidata.org/wiki/Q216378","display_name":"Usability","level":2,"score":0.4214000105857849},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.3831000030040741},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C30038468","wikidata":"https://www.wikidata.org/wiki/Q4354775","display_name":"Memorization","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.3070000112056732},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2800000011920929}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.15687","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15687","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.2605.15687","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.15687","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"large":[1],"language":[2],"models":[3],"(MLLMs)":[4],"may":[5],"memorize":[6],"sensitive":[7],"cross-modal":[8],"information":[9],"during":[10],"pretraining,":[11],"making":[12],"machine":[13],"unlearning":[14,21,63,102,114],"(MU)":[15],"crucial.":[16],"Existing":[17],"methods":[18],"typically":[19],"evaluate":[20],"effectiveness":[22,115],"based":[23],"on":[24,107,117,123],"output":[25],"deviations,":[26],"while":[27,125],"overlooking":[28],"the":[29,45,50],"generation":[30,67,120],"quality":[31,68,121],"after":[32],"unlearning.":[33],"This":[34],"can":[35],"easily":[36],"lead":[37],"to":[38],"hallucinated":[39],"or":[40],"rigid":[41],"responses,":[42],"thereby":[43,94],"affecting":[44],"usability":[46],"and":[47,83,103,119],"safety":[48],"of":[49,135],"unlearned":[51],"model.":[52],"To":[53],"address":[54],"this":[55],"issue,":[56],"we":[57],"propose":[58],"ASRU,":[59],"a":[60,70,90,96,132],"controllable":[61],"multimodal":[62],"framework":[64],"that":[65,110],"incorporates":[66],"as":[69],"core":[71],"evaluation":[72],"objective.":[73],"ASRU":[74,111],"first":[75],"induces":[76],"initial":[77],"refusal":[78,87],"behavior":[79],"through":[80],"activation":[81],"redirection,":[82],"then":[84],"optimizes":[85],"fine-grained":[86],"boundaries":[88],"using":[89,130],"customized":[91],"reward":[92],"function,":[93],"achieving":[95],"better":[97],"trade-off":[98],"between":[99],"target":[100],"knowledge":[101],"model":[104,128],"utility.":[105],"Experiments":[106],"Qwen3-VL":[108],"show":[109],"significantly":[112],"improves":[113],"(+24.6%)":[116],"average":[118,124],"(5.8X)":[122],"effectively":[126],"preserving":[127],"utility,":[129],"only":[131],"small":[133],"amount":[134],"retained":[136],"supervision":[137],"data.":[138]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-19T00:00:00"}
