{"id":"https://openalex.org/W7153219407","doi":"https://doi.org/10.48550/arxiv.2604.07802","title":"Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models","display_name":"Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models","publication_year":2026,"publication_date":"2026-04-09","ids":{"openalex":"https://openalex.org/W7153219407","doi":"https://doi.org/10.48550/arxiv.2604.07802"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.07802","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07802","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.07802","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133331043","display_name":"Shaotian Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Li, Shaotian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133345324","display_name":"Shangze Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Shangze","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133355880","display_name":"Chuancheng Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Chuancheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133343999","display_name":"Wenhua Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Wenhua","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125558267","display_name":"Yanqiu Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Yanqiu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133360172","display_name":"Xiaohan Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Xiaohan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133376158","display_name":"Fei Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Fei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133332651","display_name":"Tat-Seng Chua","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chua, Tat-Seng","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5133331043"],"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9484000205993652,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9484000205993652,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.014399999752640724,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.006899999920278788,"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/anomaly-detection","display_name":"Anomaly detection","score":0.7364000082015991},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5194000005722046},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5138000249862671},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.508899986743927},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4212999939918518},{"id":"https://openalex.org/keywords/normality","display_name":"Normality","score":0.41690000891685486},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38839998841285706}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7364000082015991},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6262999773025513},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5213000178337097},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5194000005722046},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5138000249862671},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.508899986743927},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4212999939918518},{"id":"https://openalex.org/C2776157432","wikidata":"https://www.wikidata.org/wiki/Q1375683","display_name":"Normality","level":2,"score":0.41690000891685486},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3303000032901764},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.31290000677108765},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.2587999999523163}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.07802","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07802","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.07802","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07802","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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large-scale":[0],"vision-language":[1],"models":[2,60],"(VLMs)":[3],"exhibit":[4],"remarkable":[5],"zero-shot":[6],"capabilities,":[7],"yet":[8],"the":[9,159,168],"internal":[10],"mechanisms":[11],"driving":[12],"their":[13],"anomaly":[14,53,86,156],"detection":[15,157],"(AD)":[16],"performance":[17,141],"remain":[18],"poorly":[19],"understood.":[20],"Current":[21],"methods":[22],"predominantly":[23],"treat":[24],"VLMs":[25],"as":[26,158],"black-box":[27],"feature":[28],"extractors,":[29],"assuming":[30],"that":[31,52,68,93,121,137],"anomaly-specific":[32],"knowledge":[33,54,70,87,165],"must":[34],"be":[35],"acquired":[36],"through":[37],"external":[38],"adapters":[39],"or":[40],"memory":[41],"banks.":[42],"In":[43],"this":[44,48,69],"paper,":[45],"we":[46,83],"challenge":[47],"assumption":[49],"by":[50],"arguing":[51],"is":[55,71],"intrinsically":[56],"embedded":[57],"within":[58,73],"pre-trained":[59,164],"but":[61],"remains":[62],"latent":[63,85,163],"and":[64,95],"under-activated.":[65],"We":[66],"hypothesize":[67],"concentrated":[72],"a":[74,90,103,116,152,171],"sparse":[75],"subset":[76],"of":[77,106,162,170],"anomaly-sensitive":[78],"neurons.":[79],"To":[80],"validate":[81],"this,":[82],"propose":[84],"excavation":[88],"(LAKE),":[89],"training-free":[91],"framework":[92],"identifies":[94],"elicits":[96],"these":[97,111],"critical":[98],"neuronal":[99],"signals":[100],"using":[101],"only":[102],"minimal":[104],"set":[105],"normal":[107],"samples.":[108],"By":[109],"isolating":[110],"sensitive":[112],"neurons,":[113],"LAKE":[114,138],"constructs":[115],"highly":[117],"compact":[118],"normality":[119],"representation":[120],"integrates":[122],"visual":[123],"structural":[124],"deviations":[125],"with":[126],"cross-modal":[127],"semantic":[128],"activations.":[129],"Extensive":[130],"experiments":[131],"on":[132],"industrial":[133],"AD":[134],"benchmarks":[135],"demonstrate":[136],"achieves":[139],"state-of-the-art":[140],"while":[142],"providing":[143],"intrinsic,":[144],"neuron-level":[145],"interpretability.":[146],"Ultimately,":[147],"our":[148],"work":[149],"advocates":[150],"for":[151],"paradigm":[153],"shift:":[154],"redefining":[155],"targeted":[160],"activation":[161],"rather":[166],"than":[167],"acquisition":[169],"downstream":[172],"task.":[173]},"counts_by_year":[],"updated_date":"2026-04-11T06:19:08.300824","created_date":"2026-04-11T00:00:00"}
