{"id":"https://openalex.org/W7127345311","doi":"https://doi.org/10.48550/arxiv.2602.00414","title":"Toward Autonomous Laboratory Safety Monitoring with Vision Language Models: Learning to See Hazards Through Scene Structure","display_name":"Toward Autonomous Laboratory Safety Monitoring with Vision Language Models: Learning to See Hazards Through Scene Structure","publication_year":2026,"publication_date":"2026-01-31","ids":{"openalex":"https://openalex.org/W7127345311","doi":"https://doi.org/10.48550/arxiv.2602.00414"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.00414","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","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":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Chakraborty, Trishna","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chakraborty, Trishna","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058809789","display_name":"Udita Ghosh","orcid":"https://orcid.org/0000-0001-9057-585X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ghosh, Udita","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124956137","display_name":"Aldair Ernesto Gongora","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gongora, Aldair Ernesto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021316298","display_name":"Ruben Glatt","orcid":"https://orcid.org/0000-0002-4401-3810"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Glatt, Ruben","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124925203","display_name":"Yue Dong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dong, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124910718","display_name":"Jiachen Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Jiachen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103706109","display_name":"Amit K. Roy-Chowdhury","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roy-Chowdhury, Amit K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124906564","display_name":"Chengyu Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Chengyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"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":0.8335999846458435,"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":0.8335999846458435,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.02500000037252903,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10799","display_name":"Data Visualization and Analytics","score":0.0215000007301569,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.6100000143051147},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5763000249862671},{"id":"https://openalex.org/keywords/scene-graph","display_name":"Scene graph","score":0.4490000009536743},{"id":"https://openalex.org/keywords/hazard","display_name":"Hazard","score":0.43970000743865967},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4115000069141388},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.397599995136261},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3666999936103821},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.35260000824928284}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.673799991607666},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.6100000143051147},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5763000249862671},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5390999913215637},{"id":"https://openalex.org/C179372163","wikidata":"https://www.wikidata.org/wiki/Q1406181","display_name":"Scene graph","level":3,"score":0.4490000009536743},{"id":"https://openalex.org/C49261128","wikidata":"https://www.wikidata.org/wiki/Q1132455","display_name":"Hazard","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4115000069141388},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.397599995136261},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3885999917984009},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3666999936103821},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.352400004863739},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C2777488183","wikidata":"https://www.wikidata.org/wiki/Q6900510","display_name":"Safety monitoring","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.31709998846054077},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3107999861240387},{"id":"https://openalex.org/C132835097","wikidata":"https://www.wikidata.org/wiki/Q7663745","display_name":"System safety","level":2,"score":0.2971999943256378},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.296999990940094},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2800000011920929},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.2750999927520752},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26750001311302185},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.26409998536109924}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.00414","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.00414","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.00414","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":"pmh:doi:10.48550/arxiv.2602.00414","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"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":{"Laboratories":[0],"are":[1,57],"prone":[2],"to":[3,46,158],"severe":[4],"injuries":[5],"from":[6,145],"minor":[7],"unsafe":[8],"actions,":[9],"yet":[10],"continuous":[11],"safety":[12,18,35,55],"monitoring":[13],"--":[14,20],"beyond":[15],"mandatory":[16],"pre-lab":[17],"training":[19],"is":[21,43],"limited":[22],"by":[23,164],"human":[24],"availability.":[25],"Vision":[26],"language":[27,91],"models":[28,92,100,121],"(VLMs)":[29],"offer":[30],"promise":[31],"for":[32],"autonomous":[33],"laboratory":[34,78],"monitoring,":[36],"but":[37,131],"their":[38],"effectiveness":[39],"in":[40,134,139,162,181],"realistic":[41],"settings":[42,136],"unclear":[44],"due":[45],"the":[47,106],"lack":[48],"of":[49,83,109],"visual":[50,166,182],"evaluation":[51],"data,":[52],"as":[53,60,93,101],"most":[54],"incidents":[56],"documented":[58],"primarily":[59],"unstructured":[61],"text.":[62],"To":[63,147],"address":[64],"this":[65],"gap,":[66],"we":[67,150],"first":[68],"introduce":[69],"a":[70,152],"structured":[71,141,169],"data":[72],"generation":[73,99],"pipeline":[74],"that":[75,123],"converts":[76],"textual":[77,128],"scenarios":[79,115],"into":[80,168],"aligned":[81,173],"triples":[82],"(image,":[84],"scene":[85,94,129,170],"graph,":[86,130],"ground":[87],"truth),":[88],"using":[89],"large":[90],"graph":[95],"architects":[96],"and":[97,116,119],"image":[98],"renderers.":[102],"Our":[103],"experiments":[104],"on":[105],"synthetic":[107],"dataset":[108],"1,207":[110],"samples":[111],"across":[112],"362":[113],"unique":[114],"seven":[117],"open-":[118],"closed-source":[120],"show":[122],"VLMs":[124,163],"perform":[125],"effectively":[126],"given":[127],"degrade":[132],"substantially":[133],"visual-only":[135],"indicating":[137],"difficulty":[138],"extracting":[140],"object":[142],"relationships":[143],"directly":[144],"pixels.":[146],"overcome":[148],"this,":[149],"propose":[151],"post-training":[153],"context-engineering":[154],"approach,":[155],"scene-graph-guided":[156],"alignment,":[157],"bridge":[159],"perceptual":[160],"gaps":[161],"translating":[165],"inputs":[167],"graphs":[171],"better":[172],"with":[174],"VLM":[175],"reasoning,":[176],"improving":[177],"hazard":[178],"detection":[179],"performance":[180],"only":[183],"settings.":[184]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-02-04T00:00:00"}