{"id":"https://openalex.org/W7148877047","doi":"https://doi.org/10.48550/arxiv.2604.01460","title":"Reinforcing Consistency in Video MLLMs with Structured Rewards","display_name":"Reinforcing Consistency in Video MLLMs with Structured Rewards","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148877047","doi":"https://doi.org/10.48550/arxiv.2604.01460"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.01460","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01460","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":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.2604.01460","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114202283","display_name":"Yihao Quan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quan, Yihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111036231","display_name":"Zeru Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Zeru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132912721","display_name":"Jinman Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Jinman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132868652","display_name":"Ruixiang Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Ruixiang","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":0.891700029373169,"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":0.891700029373169,"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":0.02969999983906746,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.016200000420212746,"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/consistency","display_name":"Consistency (knowledge bases)","score":0.7017999887466431},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.546500027179718},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5418999791145325},{"id":"https://openalex.org/keywords/audit","display_name":"Audit","score":0.5340999960899353},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.41850000619888306},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.3853999972343445}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7271000146865845},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.7017999887466431},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.546500027179718},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5418999791145325},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.5340999960899353},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4948999881744385},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.41850000619888306},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.3853999972343445},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.375},{"id":"https://openalex.org/C175652121","wikidata":"https://www.wikidata.org/wiki/Q4379351","display_name":"Causal consistency","level":5,"score":0.3379000127315521},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C11534374","wikidata":"https://www.wikidata.org/wiki/Q7805280","display_name":"Time consistency","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.262800008058548},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.01460","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01460","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":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.2604.01460","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.01460","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.5903105735778809,"display_name":"No poverty","id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"large":[1],"language":[2],"models":[3],"(MLLMs)":[4],"have":[5],"achieved":[6],"remarkable":[7],"progress":[8],"in":[9],"video":[10,108,190,217],"understanding.":[11,109,218],"However,":[12],"seemingly":[13],"plausible":[14],"outputs":[15],"often":[16,89,123],"suffer":[17],"from":[18,146],"poor":[19],"visual":[20],"and":[21,63,93,148,167,176,178,192],"temporal":[22,64,149,171],"grounding:":[23],"a":[24,40,52,58,68,103,142,170,180,211],"model":[25],"may":[26],"fabricate":[27],"object":[28],"existence,":[29],"assign":[30],"incorrect":[31],"attributes,":[32,166],"or":[33,44],"collapse":[34],"repeated":[35],"events":[36],"while":[37],"still":[38],"producing":[39],"globally":[41],"reasonable":[42],"caption":[43,59],"answer.":[45],"We":[46],"study":[47],"this":[48,195],"failure":[49],"mode":[50],"through":[51],"compositional":[53],"consistency":[54],"audit":[55,81],"that":[56,83,98,206],"decomposes":[57],"into":[60],"supporting":[61],"factual":[62,147,164],"claims,":[65],"investigating":[66],"whether":[67],"correct":[69,85],"high-level":[70],"prediction":[71],"is":[72,102,210],"actually":[73],"backed":[74],"by":[75],"valid":[76],"lower-level":[77],"evidence.":[78],"Our":[79,151],"top-down":[80],"reveals":[82],"even":[84],"root":[86],"relational":[87],"claims":[88],"lack":[90],"reliable":[91],"attribute":[92],"existence":[94],"support.":[95],"This":[96],"indicates":[97],"standard":[99,120],"sentence-level":[100,121,139],"supervision":[101],"weak":[104],"proxy":[105],"for":[106,117,163,173,184],"faithful":[107,216],"Furthermore,":[110],"when":[111],"turning":[112],"to":[113,127,214],"reinforcement":[114],"learning":[115],"(RL)":[116],"better":[118],"alignment,":[119],"rewards":[122,140],"prove":[124],"too":[125],"coarse":[126],"accurately":[128],"localize":[129],"specific":[130],"grounding":[131],"failures.":[132],"To":[133],"address":[134],"this,":[135],"we":[136],"replace":[137],"generic":[138],"with":[141],"structured":[143,207],"reward":[144,162,172,183,208],"built":[145],"units.":[150],"training":[152],"objective":[153,196],"integrates":[154],"three":[155],"complementary":[156],"components:":[157],"(1)":[158],"an":[159],"instance-aware":[160],"scene-graph":[161],"objects,":[165],"relations;":[168],"(2)":[169],"event":[174],"ordering":[175],"repetition;":[177],"(3)":[179],"video-grounded":[181],"VQA":[182],"hierarchical":[185],"self-verification.":[186],"Across":[187],"temporal,":[188],"general":[189],"understanding,":[191],"hallucination-oriented":[193],"benchmarks,":[194],"yields":[197],"consistent":[198],"gains":[199],"on":[200],"open-source":[201],"backbones.":[202],"These":[203],"results":[204],"suggest":[205],"shaping":[209],"practical":[212],"route":[213],"more":[215]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-04T00:00:00"}
