{"id":"https://openalex.org/W7164025272","doi":"https://doi.org/10.48550/arxiv.2606.09169","title":"IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation","display_name":"IMUG-Bench: Benchmarking Unified Multimodal Models on Interleaved Understanding and Generation","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7164025272","doi":"https://doi.org/10.48550/arxiv.2606.09169"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.09169","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09169","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.2606.09169","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138231818","display_name":"Lingyi Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Lingyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5121873825","display_name":"Zecong Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Zecong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138282502","display_name":"Haoran Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Haoran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123550941","display_name":"Tengju Ru","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ru, Tengju","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123563379","display_name":"Zhejun Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Zhejun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123606382","display_name":"Weitong Lian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lian, Weitong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138206350","display_name":"Qi Kang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang, Qi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124372781","display_name":"Hangshuo Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Hangshuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138214401","display_name":"Yichen Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Yichen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136222205","display_name":"Yechi Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yechi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138281858","display_name":"Kaixuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Kaixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138262837","display_name":"Yu-Jie Yuan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuan, Yu-Jie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138213279","display_name":"Chunwei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chunwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138217908","display_name":"Yu Zhang (12946)","orcid":"https://orcid.org/0009-0007-8877-9846"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138206268","display_name":"Bo Dai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dai, Bo","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/T10028","display_name":"Topic Modeling","score":0.44830000400543213,"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/T10028","display_name":"Topic Modeling","score":0.44830000400543213,"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":0.3781000077724457,"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/T12031","display_name":"Speech and dialogue systems","score":0.04639999940991402,"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/benchmarking","display_name":"Benchmarking","score":0.7360000014305115},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6881999969482422},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5503000020980835},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5098999738693237},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.3472000062465668}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7806000113487244},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7360000014305115},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6881999969482422},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5503000020980835},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5098999738693237},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44929999113082886},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4323999881744385},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2955999970436096},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.25839999318122864}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.09169","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09169","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.2606.09169","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09169","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"unified":[3],"multimodal":[4],"models":[5],"(UMMs)":[6],"have":[7],"emerged":[8],"to":[9,38,48],"support":[10],"both":[11],"understanding":[12,82,108],"and":[13,53,83,95,100,128,135,138,160,168,183],"generation":[14,84,145,166,173],"within":[15],"a":[16,26,68],"single":[17],"framework.":[18],"Mastering":[19],"dynamic,":[20],"multi-turn":[21,59,72,117,148,184],"interleaved":[22,73],"image-text":[23,74],"dialogues":[24],"is":[25],"crucial":[27],"task":[28],"for":[29,71],"UMMs":[30,77],"in":[31,58,147,172],"real-world":[32,116],"applications.":[33],"However,":[34],"existing":[35],"benchmarks":[36],"fail":[37],"evaluate":[39,125],"this":[40,63],"important":[41],"task,":[42],"as":[43],"they":[44],"are":[45],"often":[46],"limited":[47],"single-turn":[49],"or":[50],"static":[51],"settings,":[52],"typically":[54],"overlook":[55],"exposure":[56,141,170],"bias":[57,142,171],"interactions.":[60,149],"To":[61],"bridge":[62],"gap,":[64],"we":[65],"propose":[66],"IMUG-Bench,":[67],"comprehensive":[69],"benchmark":[70],"dialogue":[75],"of":[76,187],"that":[78,113],"jointly":[79],"evaluates":[80],"their":[81,132],"capabilities.":[85],"Our":[86],"IMUG-Bench":[87,123],"comprises":[88],"three":[89],"classes:":[90],"Static":[91],"Spatial,":[92],"Temporal":[93],"Causal,":[94],"Hybrid,":[96],"covering":[97],"3,113":[98],"samples":[99],"12,034":[101],"interaction":[102,118,185],"turns.":[103],"It":[104],"also":[105],"includes":[106],"dynamic":[107],"questions,":[109],"thereby":[110],"supporting":[111],"evaluation":[112],"better":[114],"reflects":[115],"scenarios.":[119],"Large-scale":[120],"experiments":[121],"on":[122,143],"systematically":[124],"mainstream":[126],"open-source":[127],"closed-source":[129],"UMMs,":[130],"revealing":[131],"capability":[133,186],"boundaries":[134],"failure":[136],"modes,":[137],"uncovering":[139],"pronounced":[140],"the":[144,181],"side":[146],"We":[150],"further":[151],"explore":[152],"several":[153],"test-time":[154],"scaling":[155],"strategies,":[156],"including":[157],"Chain-of-Thought,":[158],"Self-Verification,":[159],"Best-of-N":[161],"Sampling,":[162],"which":[163],"effectively":[164],"improve":[165],"accuracy":[167],"mitigate":[169],"tasks.":[174],"These":[175],"findings":[176],"provide":[177],"insights":[178],"into":[179],"enhancing":[180],"robustness":[182],"future":[188],"UMMs.":[189]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}