{"id":"https://openalex.org/W7162667313","doi":"https://doi.org/10.48550/arxiv.2605.27705","title":"AgenticVBench: Can AI Agents Complete Real-World Post-Production Tasks?","display_name":"AgenticVBench: Can AI Agents Complete Real-World Post-Production Tasks?","publication_year":2026,"publication_date":"2026-05-26","ids":{"openalex":"https://openalex.org/W7162667313","doi":"https://doi.org/10.48550/arxiv.2605.27705"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.27705","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27705","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.2605.27705","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047849718","display_name":"Zongheng Cao","orcid":"https://orcid.org/0000-0001-9419-7163"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Zongheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137213528","display_name":"Yi Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137281716","display_name":"Rui Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137254381","display_name":"Xinyu Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Xinyu","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":0.5839999914169312,"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":0.5839999914169312,"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":0.17759999632835388,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.026000000536441803,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7692000269889832},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.7534999847412109},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6837999820709229},{"id":"https://openalex.org/keywords/production","display_name":"Production (economics)","score":0.390500009059906},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.3287000060081482},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.31130000948905945}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7692000269889832},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.7534999847412109},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6837999820709229},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6093999743461609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5504000186920166},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5152999758720398},{"id":"https://openalex.org/C2778348673","wikidata":"https://www.wikidata.org/wiki/Q739302","display_name":"Production (economics)","level":2,"score":0.390500009059906},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.29589998722076416},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2822999954223633},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.27880001068115234},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.26649999618530273},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C2992770021","wikidata":"https://www.wikidata.org/wiki/Q7247850","display_name":"Production model","level":3,"score":0.251800000667572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.27705","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27705","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.2605.27705","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.27705","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":[{"score":0.5140682458877563,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Video":[0],"production":[1,57],"workflows":[2,58],"offer":[3],"a":[4,38,133],"rich":[5],"and":[6,22,29,80,92,128,137,141],"demanding":[7],"arena":[8],"for":[9,135,143],"evaluating":[10],"multimodal":[11],"AI":[12],"agents:":[13],"they":[14],"require":[15],"composite":[16],"capabilities":[17],"across":[18,44],"text,":[19],"image,":[20],"audio,":[21],"video":[23,145],"understanding,":[24],"along":[25],"with":[26,73,89],"long-horizon":[27],"planning,":[28],"tool":[30],"use.":[31],"To":[32],"this":[33],"end,":[34],"we":[35],"introduce":[36],"AgenticVBench,":[37],"benchmark":[39],"of":[40,67,118],"100":[41],"agentic":[42,144],"tasks":[43],"4":[45],"task":[46],"families":[47],"spanning":[48],"the":[49,109,116],"real":[50,56],"world":[51],"post-production":[52],"workflow,":[53],"constructed":[54],"from":[55],"contributed":[59],"by":[60],"20":[61],"industry":[62],"experts":[63],"averaging":[64],"6":[65],"years":[66],"professional":[68],"experience.":[69],"Tasks":[70],"are":[71],"paired":[72],"evaluation":[74],"specifications":[75],"that":[76,115],"combine":[77],"programmatic":[78],"verifiers":[79],"expert":[81,106],"rubrics.":[82],"We":[83,112],"evaluate":[84],"frontier":[85],"vision-language":[86],"models":[87,140],"(VLMs)":[88],"both":[90,139],"vendor-native":[91],"open-source":[93],"harnesses.":[94],"The":[95],"best":[96],"evaluated":[97],"agent":[98],"stack":[99],"barely":[100],"crosses":[101],"30%,":[102],"far":[103],"below":[104],"human":[105],"performance":[107],"on":[108],"same":[110],"tasks.":[111],"further":[113],"find":[114],"choice":[117],"harness":[119],"substantially":[120],"affects":[121],"model":[122],"behavior,":[123],"including":[124],"scores,":[125],"tool-use":[126],"patterns,":[127],"failure":[129],"modes.":[130],"AgenticVBench":[131],"provides":[132],"foundation":[134],"diagnosing":[136],"improving":[138],"harnesses":[142],"production.":[146],"Benchmark":[147],"website:":[148],"https://agenticvbench.com.":[149]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-29T00:00:00"}