{"id":"https://openalex.org/W7161745725","doi":"https://doi.org/10.48550/arxiv.2605.16457","title":"Identifiable Token Correspondence for World Models","display_name":"Identifiable Token Correspondence for World Models","publication_year":2026,"publication_date":"2026-05-15","ids":{"openalex":"https://openalex.org/W7161745725","doi":"https://doi.org/10.48550/arxiv.2605.16457"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.16457","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16457","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":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.2605.16457","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088838404","display_name":"Youngin Kim","orcid":"https://orcid.org/0000-0001-6660-2125"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Youngin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136493043","display_name":"Ray Sun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sun, Ray","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022484330","display_name":"In-Soo Kim","orcid":"https://orcid.org/0000-0001-6539-1776"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Inho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072151744","display_name":"Bumsoo Park","orcid":"https://orcid.org/0000-0003-0919-2507"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Park, Bumsoo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5085451600","display_name":"Hyun Oh Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Hyun Oh","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.3382999897003174,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.3382999897003174,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.23649999499320984,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.1404000073671341,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.8967000246047974},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5982999801635742},{"id":"https://openalex.org/keywords/token-passing","display_name":"Token passing","score":0.5318999886512756},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5156999826431274},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4975000023841858},{"id":"https://openalex.org/keywords/copying","display_name":"Copying","score":0.4964999854564667},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.49140000343322754},{"id":"https://openalex.org/keywords/object-permanence","display_name":"Object permanence","score":0.44839999079704285}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.8967000246047974},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7116000056266785},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5982999801635742},{"id":"https://openalex.org/C115067241","wikidata":"https://www.wikidata.org/wiki/Q1639854","display_name":"Token passing","level":3,"score":0.5318999886512756},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5156999826431274},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5135999917984009},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4975000023841858},{"id":"https://openalex.org/C2779151265","wikidata":"https://www.wikidata.org/wiki/Q1156791","display_name":"Copying","level":2,"score":0.4964999854564667},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.49140000343322754},{"id":"https://openalex.org/C22304111","wikidata":"https://www.wikidata.org/wiki/Q1417978","display_name":"Object permanence","level":4,"score":0.44839999079704285},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41999998688697815},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.35429999232292175},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3522999882698059},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.33869999647140503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3310000002384186},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28760001063346863},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.26739999651908875},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.16457","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16457","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":"doi:10.48550/arxiv.2605.16457","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.16457","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.5128851532936096,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Token-based":[0],"transformer":[1,63,103],"world":[2,64],"models":[3,65],"have":[4],"shown":[5],"strong":[6],"performance":[7,122],"in":[8,18],"visual":[9],"reinforcement":[10],"learning,":[11],"but":[12],"often":[13],"suffer":[14],"from":[15,90],"temporal":[16],"inconsistency":[17],"long-horizon":[19],"rollouts,":[20],"including":[21],"object":[22],"duplication,":[23],"disappearance,":[24],"and":[25,105,109,135,151],"transmutation.":[26],"A":[27],"key":[28],"reason":[29],"is":[30,83],"that":[31,66],"most":[32],"existing":[33,116],"approaches":[34],"treat":[35],"next-frame":[36,68,81],"prediction":[37,69],"purely":[38],"as":[39,70],"a":[40,58,71,88,97,131,136],"token":[41,77,82,89],"generation":[42],"problem,":[43],"without":[44],"considering":[45],"the":[46,91,102,141,146],"persistence":[47],"of":[48,115,133,138,149],"tokens":[49],"across":[50],"time.":[51],"We":[52,153],"introduce":[53],"Identifiable":[54],"Token":[55],"Correspondence":[56],"(ITC),":[57],"decoding":[59],"step":[60],"for":[61],"token-based":[62],"formulates":[67],"structured":[72],"assignment":[73],"problem":[74],"with":[75],"latent":[76],"correspondence":[78],"variables:":[79],"each":[80],"explained":[84],"either":[85],"by":[86,95],"copying":[87],"previous":[92,147],"frame":[93],"or":[94],"generating":[96],"new":[98],"one.":[99],"ITC":[100],"leaves":[101],"architecture":[104],"training":[106],"procedure":[107],"unchanged":[108],"can":[110],"be":[111],"added":[112],"on":[113,123,140,158],"top":[114],"backbones.":[117],"Our":[118],"experiments":[119],"show":[120],"state-of-the-art":[121],"4":[124],"challenging":[125],"benchmarks.":[126],"The":[127],"proposed":[128],"method":[129],"achieves":[130],"return":[132],"72.5%":[134],"score":[137],"35.6%":[139],"Craftax-classic":[142],"benchmark,":[143],"significantly":[144],"surpassing":[145],"best":[148],"67.4%":[150],"27.9%.":[152],"release":[154],"our":[155],"source":[156],"code":[157],"https://github.com/snu-mllab/Identifiable-Token-Correspondence.":[159]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-20T00:00:00"}
