{"id":"https://openalex.org/W7140143831","doi":"https://doi.org/10.48550/arxiv.2603.19466","title":"ProactiveBench: Benchmarking Proactiveness in Multimodal Large Language Models","display_name":"ProactiveBench: Benchmarking Proactiveness in Multimodal Large Language Models","publication_year":2026,"publication_date":"2026-03-19","ids":{"openalex":"https://openalex.org/W7140143831","doi":"https://doi.org/10.48550/arxiv.2603.19466"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.19466","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19466","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":null,"license_id":null,"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.2603.19466","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5023771218","display_name":"Thomas De Min","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"De Min, Thomas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037398393","display_name":"Subhankar Roy","orcid":"https://orcid.org/0009-0008-2395-8111"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roy, Subhankar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130401026","display_name":"St\u00e9phane Lathuili\u00e8re","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lathuili\u00e8re, St\u00e9phane","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130366756","display_name":"Elisa Ricci","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ricci, Elisa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130386730","display_name":"Massimiliano Mancini","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mancini, Massimiliano","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":0.5942999720573425,"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":0.5942999720573425,"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":0.10779999941587448,"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":0.02850000001490116,"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/proactivity","display_name":"Proactivity","score":0.9850000143051147},{"id":"https://openalex.org/keywords/conversation","display_name":"Conversation","score":0.7257000207901001},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5867999792098999},{"id":"https://openalex.org/keywords/ask-price","display_name":"Ask price","score":0.5663999915122986},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5073999762535095},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.44850000739097595},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.3885999917984009},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.3723999857902527}],"concepts":[{"id":"https://openalex.org/C142944206","wikidata":"https://www.wikidata.org/wiki/Q1786137","display_name":"Proactivity","level":2,"score":0.9850000143051147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7303000092506409},{"id":"https://openalex.org/C2777200299","wikidata":"https://www.wikidata.org/wiki/Q52943","display_name":"Conversation","level":2,"score":0.7257000207901001},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5867999792098999},{"id":"https://openalex.org/C90329073","wikidata":"https://www.wikidata.org/wiki/Q914232","display_name":"Ask price","level":2,"score":0.5663999915122986},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.5152999758720398},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5073999762535095},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4731000065803528},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.44850000739097595},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3885999917984009},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3481000065803528},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.33399999141693115},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.30799999833106995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29739999771118164},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C2780829048","wikidata":"https://www.wikidata.org/wiki/Q1624720","display_name":"Conversation analysis","level":3,"score":0.2736999988555908},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.19466","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19466","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.19466","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.19466","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":null,"license_id":null,"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":{"Effective":[0],"collaboration":[1],"begins":[2],"with":[3,89],"knowing":[4],"when":[5,12],"to":[6,14,24,135],"ask":[7,22],"for":[8],"help.":[9],"For":[10],"example,":[11],"trying":[13],"identify":[15],"an":[16],"occluded":[17,62],"object,":[18],"a":[19,31,46,117,143],"human":[20],"would":[21],"someone":[23],"remove":[25],"the":[26],"obstruction.":[27],"Can":[28],"MLLMs":[29,74],"exhibit":[30],"similar":[32],"\"proactive\"":[33],"behavior":[34],"by":[35],"requesting":[36],"simple":[37,118],"user":[38],"interventions?":[39],"To":[40],"investigate":[41],"this,":[42],"we":[43,101,115],"introduce":[44,109],"ProactiveBench,":[45,76],"benchmark":[47],"built":[48],"from":[49],"seven":[50],"repurposed":[51],"datasets":[52],"that":[53,78,103,128],"tests":[54],"proactiveness":[55,85,95,129],"across":[56],"different":[57],"tasks":[58],"such":[59],"as":[60,142],"recognizing":[61],"objects,":[63],"enhancing":[64],"image":[65],"quality,":[66],"and":[67,106],"interpreting":[68],"coarse":[69],"sketches.":[70],"We":[71,138],"evaluate":[72],"22":[73],"on":[75,122],"showing":[77],"(i)":[79],"they":[80],"generally":[81],"lack":[82],"proactiveness;":[83],"(ii)":[84],"does":[86],"not":[87],"correlate":[88],"model":[90],"capacity;":[91],"(iii)":[92],"\"hinting\"":[93],"at":[94],"yields":[96],"only":[97],"marginal":[98],"gains.":[99],"Surprisingly,":[100],"found":[102],"conversation":[104],"histories":[105],"in-context":[107],"learning":[108],"negative":[110],"biases,":[111],"hindering":[112],"performance.":[113],"Finally,":[114],"explore":[116],"fine-tuning":[119],"strategy":[120],"based":[121],"reinforcement":[123],"learning:":[124],"its":[125],"results":[126],"suggest":[127],"can":[130],"be":[131],"learned,":[132],"even":[133],"generalizing":[134],"unseen":[136],"scenarios.":[137],"publicly":[139],"release":[140],"ProactiveBench":[141],"first":[144],"step":[145],"toward":[146],"building":[147],"proactive":[148],"multimodal":[149],"models.":[150]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-24T00:00:00"}