{"id":"https://openalex.org/W7128680318","doi":"https://doi.org/10.48550/arxiv.2602.10575","title":"MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual Reinforcement Learning","display_name":"MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual Reinforcement Learning","publication_year":2026,"publication_date":"2026-02-11","ids":{"openalex":"https://openalex.org/W7128680318","doi":"https://doi.org/10.48550/arxiv.2602.10575"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.10575","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/A5125764024","display_name":"Chenhao Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Chenhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125707773","display_name":"Yazhe Niu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu, Yazhe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125684796","display_name":"Hongsheng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Hongsheng","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":0.16730908,"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":0.9763000011444092,"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":0.9763000011444092,"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/T11148","display_name":"Language, Metaphor, and Cognition","score":0.003700000001117587,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0026000000070780516,"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/grasp","display_name":"GRASP","score":0.6359000205993652},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6320000290870667},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.5651999711990356},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5315999984741211},{"id":"https://openalex.org/keywords/question-answering","display_name":"Question answering","score":0.46309998631477356},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4544000029563904},{"id":"https://openalex.org/keywords/metaphor","display_name":"Metaphor","score":0.4124999940395355},{"id":"https://openalex.org/keywords/visual-learning","display_name":"Visual learning","score":0.3898000121116638}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7106000185012817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6514999866485596},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.6359000205993652},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6320000290870667},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.5651999711990356},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5315999984741211},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.46309998631477356},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4544000029563904},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.448199987411499},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42820000648498535},{"id":"https://openalex.org/C2778311575","wikidata":"https://www.wikidata.org/wiki/Q18534","display_name":"Metaphor","level":2,"score":0.4124999940395355},{"id":"https://openalex.org/C2779321571","wikidata":"https://www.wikidata.org/wiki/Q7936605","display_name":"Visual learning","level":2,"score":0.3898000121116638},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3873000144958496},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.36660000681877136},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C2777617010","wikidata":"https://www.wikidata.org/wiki/Q18957","display_name":"Mainstream","level":2,"score":0.3294999897480011},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.27950000762939453},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2630999982357025},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.251800000667572}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.10575","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.2602.10575","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.10575","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":"pmh:doi:10.48550/arxiv.2602.10575","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":[{"display_name":"Quality Education","score":0.6807070970535278,"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":{"Metaphorical":[0],"comprehension":[1],"in":[2,38],"images":[3],"remains":[4],"a":[5,174],"critical":[6],"challenge":[7],"for":[8,48,79],"Nowadays":[9],"AI":[10],"systems.":[11],"While":[12],"Multimodal":[13],"Large":[14],"Language":[15],"Models":[16],"(MLLMs)":[17],"excel":[18],"at":[19,210],"basic":[20],"Visual":[21],"Question":[22,137],"Answering":[23],"(VQA),":[24],"they":[25],"consistently":[26],"struggle":[27],"to":[28],"grasp":[29],"the":[30,45,71,89,93,99,122,143,161,166,185,195],"nuanced":[31],"cultural,":[32],"emotional,":[33],"and":[34,54,98,138,184,191,207],"contextual":[35],"implications":[36],"embedded":[37],"visual":[39,74,94,168],"content.":[40],"This":[41],"difficulty":[42],"stems":[43],"from":[44],"task's":[46],"demand":[47],"sophisticated":[49],"multi-hop":[50],"reasoning,":[51],"cultural":[52],"context,":[53],"Theory":[55],"of":[56,119,177,187,198],"Mind":[57],"(ToM)":[58],"capabilities,":[59],"which":[60],"current":[61],"models":[62],"lack.":[63],"To":[64],"fill":[65],"this":[66],"gap,":[67],"we":[68],"propose":[69],"MetaphorStar,":[70],"first":[72],"end-to-end":[73],"reinforcement":[75],"learning":[76,156],"(RL)":[77],"framework":[78,84],"image":[80,123,157],"implication":[81,124,158],"tasks.":[82],"Our":[83,103],"includes":[85],"three":[86],"core":[87],"components:":[88],"fine-grained":[90],"dataset":[91],"TFQ-Data,":[92,112],"RL":[95],"method":[96,208],"TFQ-GRPO,":[97],"well-structured":[100],"benchmark":[101],"TFQ-Bench.":[102],"fully":[104],"open-source":[105],"MetaphorStar":[106],"family,":[107],"trained":[108],"using":[109],"TFQ-GRPO":[110],"on":[111,121,135,148],"significantly":[113,141],"improves":[114,160],"performance":[115],"by":[116],"an":[117],"average":[118],"82.6%":[120],"benchmarks.":[125],"Compared":[126],"with":[127],"20+":[128],"mainstream":[129],"MLLMs,":[130],"MetaphorStar-32B":[131],"achieves":[132],"state-of-the-art":[133],"(SOTA)":[134],"Multiple-Choice":[136],"Open-Style":[139],"Question,":[140],"outperforms":[142],"top":[144],"closed-source":[145],"model":[146,178,189,204],"Gemini-3.0-pro":[147],"True-False":[149],"Question.":[150],"Crucially,":[151],"our":[152,199],"experiments":[153],"reveal":[154],"that":[155],"tasks":[159],"general":[162],"understanding":[163],"ability,":[164],"especially":[165],"complex":[167],"reasoning":[169],"ability.":[170],"We":[171,201],"further":[172],"provide":[173],"systematic":[175],"analysis":[176],"parameter":[179],"scaling,":[180,183],"training":[181,192],"data":[182],"impact":[186],"different":[188],"architectures":[190],"strategies,":[193],"demonstrating":[194],"broad":[196],"applicability":[197],"method.":[200],"open-sourced":[202],"all":[203],"weights,":[205],"datasets,":[206],"code":[209],"https://metaphorstar.github.io.":[211]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-13T00:00:00"}