{"id":"https://openalex.org/W7134047615","doi":"https://doi.org/10.48550/arxiv.2603.05256","title":"Wiki-R1: Incentivizing Multimodal Reasoning for Knowledge-based VQA via Data and Sampling Curriculum","display_name":"Wiki-R1: Incentivizing Multimodal Reasoning for Knowledge-based VQA via Data and Sampling Curriculum","publication_year":2026,"publication_date":"2026-03-05","ids":{"openalex":"https://openalex.org/W7134047615","doi":"https://doi.org/10.48550/arxiv.2603.05256"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2603.05256","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":"article","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":"https://openalex.org/A5128249999","display_name":"Shan Ning","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ning, Shan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Qiu, Longtian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qiu, Longtian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128270274","display_name":"Xuming He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Xuming","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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28597837,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.9901000261306763,"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":0.9901000261306763,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.002300000051036477,"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/T10028","display_name":"Topic Modeling","score":0.001500000013038516,"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/bridging","display_name":"Bridging (networking)","score":0.6146000027656555},{"id":"https://openalex.org/keywords/curriculum","display_name":"Curriculum","score":0.5367000102996826},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.5062999725341797},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4934000074863434},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.45339998602867126},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.44130000472068787},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.415800005197525},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.3846000134944916}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7343999743461609},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6754000186920166},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.6146000027656555},{"id":"https://openalex.org/C47177190","wikidata":"https://www.wikidata.org/wiki/Q207137","display_name":"Curriculum","level":2,"score":0.5367000102996826},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5062999725341797},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4934000074863434},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.45339998602867126},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.44130000472068787},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4221000075340271},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.415800005197525},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4156999886035919},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.3846000134944916},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.36809998750686646},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.3472000062465668},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C2993776861","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Open domain","level":3,"score":0.289000004529953},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.2667999863624573},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.2621999979019165}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2603.05256","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.2603.05256","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.05256","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":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2603.05256","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":[{"score":0.8302358388900757,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Knowledge-Based":[0],"Visual":[1],"Question":[2],"Answering":[3],"(KB-VQA)":[4],"requires":[5],"models":[6,44],"to":[7,21,95,110,127,143,146,169,177],"answer":[8],"questions":[9],"about":[10],"an":[11],"image":[12],"by":[13],"integrating":[14],"external":[15],"knowledge,":[16],"posing":[17],"significant":[18],"challenges":[19],"due":[20],"noisy":[22],"retrieval":[23],"and":[24,49,117,141,156,174],"the":[25,30,54,86,91,96,108],"structured,":[26],"encyclopedic":[27],"nature":[28],"of":[29,81],"knowledge":[31],"base.":[32],"These":[33],"characteristics":[34],"create":[35],"a":[36,63,79,118],"distributional":[37],"gap":[38,92],"from":[39,93,167,175],"pretrained":[40],"multimodal":[41],"large":[42],"language":[43],"(MLLMs),":[45],"making":[46],"effective":[47],"reasoning":[48,72],"domain":[50],"adaptation":[51],"difficult":[52],"in":[53,73],"post-training":[55],"stage.":[56],"In":[57],"this":[58],"work,":[59],"we":[60],"propose":[61],"\\textit{Wiki-R1},":[62],"data-generation-based":[64],"curriculum":[65,103],"reinforcement":[66],"learning":[67],"framework":[68],"that":[69,122,159],"systematically":[70],"incentivizes":[71],"MLLMs":[74],"for":[75],"KB-VQA.":[76],"Wiki-R1":[77,160],"constructs":[78],"sequence":[80],"training":[82],"distributions":[83],"aligned":[84],"with":[85],"model's":[87],"evolving":[88],"capability,":[89],"bridging":[90],"pretraining":[94],"KB-VQA":[97,152],"target":[98],"distribution.":[99],"We":[100],"introduce":[101],"\\textit{controllable":[102],"data":[104],"generation},":[105],"which":[106],"manipulates":[107],"retriever":[109],"produce":[111],"samples":[112,125,145],"at":[113,186],"desired":[114],"difficulty":[115,135],"levels,":[116],"\\textit{curriculum":[119],"sampling":[120],"strategy}":[121],"selects":[123],"informative":[124],"likely":[126],"yield":[128],"non-zero":[129],"advantages":[130],"during":[131],"RL":[132],"updates.":[133],"Sample":[134],"is":[136,184],"estimated":[137],"using":[138],"observed":[139],"rewards":[140],"propagated":[142],"unobserved":[144],"guide":[147],"learning.":[148],"Experiments":[149],"on":[150,171,179],"two":[151],"benchmarks,":[153],"Encyclopedic":[154,172],"VQA":[155,173],"InfoSeek,":[157],"demonstrate":[158],"achieves":[161],"new":[162],"state-of-the-art":[163],"results,":[164],"improving":[165],"accuracy":[166],"35.5\\%":[168],"37.1\\%":[170],"40.1\\%":[176],"44.1\\%":[178],"InfoSeek.":[180],"The":[181],"project":[182],"page":[183],"available":[185],"https://artanic30.github.io/project_pages/WikiR1/.":[187]},"counts_by_year":[],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2026-03-07T00:00:00"}
