{"id":"https://openalex.org/W7162800714","doi":"https://doi.org/10.48550/arxiv.2605.30344","title":"Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection","display_name":"Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162800714","doi":"https://doi.org/10.48550/arxiv.2605.30344"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30344","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30344","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.30344","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004900217","display_name":"Xiaona Zhou","orcid":"https://orcid.org/0000-0001-5101-6780"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Xiaona","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086474262","display_name":"Muntasir Wahed","orcid":"https://orcid.org/0000-0002-9948-023X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wahed, Muntasir","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032986065","display_name":"Tianjiao Yu","orcid":"https://orcid.org/0000-0002-2530-9296"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Tianjiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006190690","display_name":"Constantin Brif","orcid":"https://orcid.org/0000-0003-1134-4952"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Brif, Constantin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5043962698","display_name":"Ismini Lourentzou","orcid":"https://orcid.org/0000-0002-1238-772X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lourentzou, Ismini","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.8510000109672546,"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":0.8510000109672546,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.0364999994635582,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.020600000396370888,"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/benchmark","display_name":"Benchmark (surveying)","score":0.8331000208854675},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7750999927520752},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.7041000127792358},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.59170001745224},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.4262000024318695}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8331000208854675},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7750999927520752},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.7041000127792358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6779000163078308},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.59170001745224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5296000242233276},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43220001459121704},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.4262000024318695},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40799999237060547},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2678999900817871}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30344","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30344","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.30344","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30344","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":0.4368073046207428}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,30,128],"Vision-Language":[3],"Models":[4],"(VLMs)":[5],"have":[6],"achieved":[7],"impressive":[8],"performance":[9,18],"across":[10],"many":[11],"tasks,":[12],"yet":[13],"prior":[14],"studies":[15],"report":[16],"unsatisfactory":[17],"when":[19],"applying":[20],"large":[21,80],"language":[22],"or":[23],"multimodal":[24],"models":[25],"to":[26,48,51],"finding":[27],"abnormal":[28],"patterns":[29],"sequential":[31],"data.":[32],"Public":[33],"anomaly":[34,75,99,111],"detection":[35],"benchmarks":[36],"typically":[37],"provide":[38],"interval":[39],"annotations":[40],"but":[41],"not":[42],"natural-language":[43],"rationales,":[44],"making":[45],"it":[46],"difficult":[47],"fine-tune":[49],"VLMs":[50,81],"produce":[52],"grounded,":[53],"interpretable":[54],"decisions.":[55],"To":[56],"address":[57],"this":[58,89],"gap,":[59],"we":[60,91],"construct":[61],"VisAnomBench,":[62],"a":[63,94],"curated":[64],"benchmark":[65,138],"built":[66],"from":[67,78],"public":[68],"time-series":[69,98],"datasets":[70],"and":[71,113,124,130,147,151],"augmented":[72],"with":[73,118,143],"high-quality":[74],"explanations":[76],"selected":[77],"multiple":[79],"using":[82],"fine-grained,":[83],"task-specific":[84],"rewards.":[85],"Through":[86],"fine-tuning":[87],"on":[88,103,135],"benchmark,":[90],"develop":[92],"VisAnomReasoner,":[93],"parameter-efficient":[95],"VLM":[96],"for":[97],"detection.":[100],"Experimental":[101],"results":[102],"VisAnomBench":[104],"show":[105],"that":[106],"VisAnomReasoner":[107,144],"achieves":[108],"more":[109],"accurate":[110],"localization":[112],"consistently":[114],"outperforms":[115],"all":[116],"baselines,":[117],"improvements":[119],"of":[120],"at":[121],"least":[122],"21.23":[123],"23.87":[125],"percentage":[126,153],"points":[127],"precision":[129,146],"F1,":[131],"respectively.":[132,155],"Additional":[133],"experiments":[134],"the":[136],"TSB-AD-U":[137],"demonstrate":[139],"strong":[140],"cross-benchmark":[141],"generalization,":[142],"improving":[145],"F1":[148],"by":[149],"9.57":[150],"13.39":[152],"points,":[154]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-30T00:00:00"}