{"id":"https://openalex.org/W7162683510","doi":"https://doi.org/10.1145/3774905.3793912","title":"Out-of-distribution Generalized Generative AI","display_name":"Out-of-distribution Generalized Generative AI","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162683510","doi":"https://doi.org/10.1145/3774905.3793912"},"language":null,"primary_location":{"id":"doi:10.1145/3774905.3793912","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774905.3793912","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3774905.3793912","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015622958","display_name":"X Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Wang","raw_affiliation_strings":["DCST and BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-0351-2939","affiliations":[{"raw_affiliation_string":"DCST and BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074869600","display_name":"Zirui Pan","orcid":"https://orcid.org/0000-0002-6795-0620"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zirui Pan","raw_affiliation_strings":["DCST, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6795-0620","affiliations":[{"raw_affiliation_string":"DCST, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101273536","display_name":"Yuwei Zhou","orcid":"https://orcid.org/0000-0001-9582-7331"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuwei Zhou","raw_affiliation_strings":["DCST, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9582-7331","affiliations":[{"raw_affiliation_string":"DCST, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135742670","display_name":"Wenwu Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenwu Zhu","raw_affiliation_strings":["DCST and BNRist, Tsinghua University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-2236-9290","affiliations":[{"raw_affiliation_string":"DCST and BNRist, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.65606936,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"19","last_page":"22"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.26759999990463257,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.26759999990463257,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.05909999832510948,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.03590000048279762,"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/generative-grammar","display_name":"Generative grammar","score":0.7199000120162964},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5281999707221985},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.49470001459121704},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.48190000653266907},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4781000018119812}],"concepts":[{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7199000120162964},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7167999744415283},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5281999707221985},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.510200023651123},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.49470001459121704},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.48190000653266907},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4781000018119812},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4366999864578247},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.3513000011444092},{"id":"https://openalex.org/C184408114","wikidata":"https://www.wikidata.org/wiki/Q1502022","display_name":"Generative Design","level":3,"score":0.3411000072956085},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3111000061035156},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3774905.3793912","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774905.3793912","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3774905.3793912","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3774905.3793912","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Companion Proceedings of the ACM Web Conference 2026","raw_type":"proceedings-article"},"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":{"This":[0,115],"tutorial":[1,116],"aims":[2],"to":[3,79,109,123,138],"disseminate":[4],"and":[5,29,49,85,100,105,120,141,149],"promote":[6],"recent":[7,118],"research":[8],"advancements":[9],"in":[10,60,155],"multi-modal":[11,21,27,52,153],"generative":[12,113],"AI,":[13],"focusing":[14],"on":[15],"two":[16,128],"dominant":[17],"families":[18],"of":[19,40,97,112],"techniques:":[20],"large":[22],"language":[23],"models":[24,31],"(MLLMs)":[25],"for":[26,32,51,151],"understanding":[28,148],"diffusion-based":[30],"visual":[33],"generation.":[34],"We":[35,54],"provide":[36],"a":[37,80],"systematic":[38],"overview":[39],"these":[41,125],"models,":[42],"covering":[43],"their":[44],"probabilistic":[45],"formulations,":[46],"architectural":[47],"designs,":[48],"mechanisms":[50],"interaction.":[53],"then":[55],"formalize":[56],"out-of-distribution":[57],"(OOD)":[58],"environments":[59],"post-training":[61,133],"as":[62],"settings":[63],"where":[64],"the":[65,95,110],"data":[66],"distribution":[67,93],"encountered":[68],"at":[69],"inference":[70],"differs":[71],"from":[72,92,127],"that":[73,135,145],"used":[74],"during":[75],"model":[76],"adaptation,":[77],"leading":[78],"mismatch":[81],"between":[82],"training-time":[83],"optimality":[84],"deployment-time":[86],"requirements.":[87],"Such":[88],"OOD":[89,139,156],"conditions":[90],"arise":[91],"shifts,":[94],"emergence":[96],"novel":[98],"concepts,":[99],"increasingly":[101],"complex":[102,152],"application":[103],"scenarios,":[104],"pose":[106],"fundamental":[107],"challenges":[108,126],"generalization":[111],"models.":[114],"explores":[117],"solutions":[119],"future":[121],"directions":[122],"address":[124],"complementary":[129],"perspectives:":[130],"(i)":[131],"generalizable":[132],"techniques":[134],"enable":[136],"adaptation":[137],"environments,":[140],"(ii)":[142],"unified":[143],"frameworks":[144],"support":[146],"both":[147],"generation":[150],"tasks":[154],"environments.":[157]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-05-29T00:00:00"}