{"id":"https://openalex.org/W7147184894","doi":"https://doi.org/10.48550/arxiv.2603.29211","title":"Xuanwu: Evolving General Multimodal Models into an Industrial-Grade Foundation for Content Ecosystems","display_name":"Xuanwu: Evolving General Multimodal Models into an Industrial-Grade Foundation for Content Ecosystems","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7147184894","doi":"https://doi.org/10.48550/arxiv.2603.29211"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.29211","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29211","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","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.2603.29211","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132642906","display_name":"Zhiqian Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhiqian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132628913","display_name":"Xu Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Xu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132700913","display_name":"Xiaoqing Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Xiaoqing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132647684","display_name":"Guangdong Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Guangdong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132582371","display_name":"Weijia Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Weijia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132716894","display_name":"Xiaolei Lv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lv, Xiaolei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132615433","display_name":"Bo Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Bo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132686092","display_name":"Jun Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Jun","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/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.44760000705718994,"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/T12262","display_name":"Hate Speech and Cyberbullying Detection","score":0.44760000705718994,"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":0.10980000346899033,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.07490000128746033,"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/software-deployment","display_name":"Software deployment","score":0.6075999736785889},{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.5573999881744385},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5343999862670898},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.5307999849319458},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.48890000581741333},{"id":"https://openalex.org/keywords/business-model","display_name":"Business model","score":0.4212000072002411},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.3734000027179718},{"id":"https://openalex.org/keywords/business-ecosystem","display_name":"Business ecosystem","score":0.33880001306533813}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6959999799728394},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.6075999736785889},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.5573999881744385},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5343999862670898},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.5307999849319458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5296000242233276},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.48890000581741333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43689998984336853},{"id":"https://openalex.org/C4216890","wikidata":"https://www.wikidata.org/wiki/Q815823","display_name":"Business model","level":2,"score":0.4212000072002411},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.38499999046325684},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.3734000027179718},{"id":"https://openalex.org/C167908162","wikidata":"https://www.wikidata.org/wiki/Q870119","display_name":"Business ecosystem","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C2777617010","wikidata":"https://www.wikidata.org/wiki/Q18957","display_name":"Mainstream","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C172191483","wikidata":"https://www.wikidata.org/wiki/Q1071806","display_name":"Provisioning","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C93225998","wikidata":"https://www.wikidata.org/wiki/Q1941972","display_name":"Moderation","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29440000653266907},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2646999955177307},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C42475967","wikidata":"https://www.wikidata.org/wiki/Q194292","display_name":"Operations research","level":1,"score":0.26350000500679016},{"id":"https://openalex.org/C193669473","wikidata":"https://www.wikidata.org/wiki/Q5001867","display_name":"Business domain","level":5,"score":0.2590999901294708},{"id":"https://openalex.org/C2777601897","wikidata":"https://www.wikidata.org/wiki/Q3409113","display_name":"Presentation (obstetrics)","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C539667460","wikidata":"https://www.wikidata.org/wiki/Q2414942","display_name":"Management science","level":1,"score":0.2531000077724457},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.29211","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29211","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.29211","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.29211","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":"public-domain","license_id":"https://openalex.org/licenses/public-domain","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,43],"recent":[1],"years,":[2],"multimodal":[3,57,146],"large":[4],"models":[5,22,58],"have":[6],"continued":[7],"to":[8],"improve":[9],"on":[10,172],"general":[11,56,104,203],"benchmarks.":[12],"However,":[13],"in":[14,175],"real-world":[15],"content":[16,68],"moderation":[17,163],"and":[18,28,37,88,111,114,125,129,165,206],"adversarial":[19,177],"settings,":[20],"mainstream":[21],"still":[23],"suffer":[24],"from":[25],"degraded":[26],"generalization":[27],"catastrophic":[29],"forgetting":[30],"because":[31],"of":[32,40,54,103,141,157,170],"limited":[33,189],"fine-grained":[34,83],"visual":[35,84,201],"perception":[36],"insufficient":[38],"modeling":[39],"long-tail":[41],"noise.":[42],"this":[44],"paper,":[45],"we":[46,106],"present":[47],"Xuanwu":[48,135,192],"VL-2B":[49,136,193],"as":[50],"a":[51,73,108,119,166,188,195],"case":[52],"study":[53],"how":[55],"can":[59],"be":[60],"developed":[61,107],"into":[62],"an":[63,92,138,154],"industrial-grade":[64],"foundation":[65],"model":[66,71,117],"for":[67,150],"ecosystems.":[69],"The":[70],"adopts":[72],"compact":[74],"InternViT-300M":[75],"+":[76,78],"MLP":[77],"Qwen3":[79],"1.7B":[80],"architecture,":[81],"balancing":[82],"perception,":[85,202],"language-semantic":[86],"alignment,":[87,200],"deployment":[89,207],"cost":[90],"within":[91],"approximately":[93],"2B-parameter":[94],"budget.":[95],"To":[96],"balance":[97,197],"business":[98,131,162,199],"specialization":[99],"with":[100],"the":[101,116],"retention":[102],"capabilities,":[105],"data":[109],"iteration":[110],"curation":[112],"mechanism":[113],"trained":[115],"through":[118],"progressive":[120],"three-stage":[121],"pipeline:":[122],"pre-training,":[123],"mid-training,":[124],"post-training.":[126],"Ablation":[127],"studies":[128],"offline":[130],"evaluations":[132],"show":[133,185],"that":[134],"achieves":[137,194],"average":[139,155],"score":[140],"67.90":[142],"across":[143],"seven":[144,160],"OpenCompass":[145],"metrics":[147],"(vs.":[148],"64.27":[149],"InternVL":[151],"3.5":[152],"2B),":[153],"recall":[156,169],"94.38%":[158],"over":[159],"independent":[161],"tasks,":[164],"weighted":[167],"overall":[168],"82.82%":[171],"policy-violating":[173],"text":[174],"challenging":[176],"OCR":[178],"scenarios,":[179],"outperforming":[180],"Gemini-2.5-Pro":[181],"(76.72%).":[182],"These":[183],"results":[184],"that,":[186],"under":[187],"parameter":[190],"budget,":[191],"practical":[196],"among":[198],"capability":[204],"retention,":[205],"cost.":[208]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-02T00:00:00"}
