{"id":"https://openalex.org/W7165898117","doi":"https://doi.org/10.48550/arxiv.2606.24893","title":"AgentOdyssey: Open-Ended Long-Horizon Text Game Generation for Test-Time Continual Learning Agents","display_name":"AgentOdyssey: Open-Ended Long-Horizon Text Game Generation for Test-Time Continual Learning Agents","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7165898117","doi":"https://doi.org/10.48550/arxiv.2606.24893"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.24893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24893","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":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.2606.24893","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139334913","display_name":"Zheyuan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zheyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136082602","display_name":"Zehao Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Zehao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030897296","display_name":"A M Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Alvin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087779128","display_name":"A F Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Andrew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139357953","display_name":"Jianwen Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Jianwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139344909","display_name":"Daniel Khashabi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khashabi, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139376122","display_name":"Tianmin Shu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shu, Tianmin","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":0.34880000352859497,"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":0.34880000352859497,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.21459999680519104,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.094200000166893,"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/inference","display_name":"Inference","score":0.6086999773979187},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.553600013256073},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.520799994468689},{"id":"https://openalex.org/keywords/test","display_name":"Test (biology)","score":0.4171000123023987},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.39739999175071716},{"id":"https://openalex.org/keywords/plan","display_name":"Plan (archaeology)","score":0.39149999618530273},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.3675999939441681}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7347999811172485},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6086999773979187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5867999792098999},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.553600013256073},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.520799994468689},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.4171000123023987},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3986000120639801},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.39149999618530273},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C10431821","wikidata":"https://www.wikidata.org/wiki/Q6510174","display_name":"Learning effect","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3109999895095825},{"id":"https://openalex.org/C183759332","wikidata":"https://www.wikidata.org/wiki/Q343680","display_name":"Action learning","level":4,"score":0.28679999709129333},{"id":"https://openalex.org/C3018412434","wikidata":"https://www.wikidata.org/wiki/Q7889","display_name":"Video game","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C88576662","wikidata":"https://www.wikidata.org/wiki/Q18646","display_name":"Episodic memory","level":3,"score":0.2662000060081482},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C37228920","wikidata":"https://www.wikidata.org/wiki/Q1307600","display_name":"Experiential learning","level":2,"score":0.2563999891281128},{"id":"https://openalex.org/C74072328","wikidata":"https://www.wikidata.org/wiki/Q1142726","display_name":"Intelligent agent","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.24893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24893","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":"doi:10.48550/arxiv.2606.24893","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24893","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.6626729965209961}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"For":[0],"agents":[1,85],"to":[2,17],"learn":[3],"continuously":[4],"from":[5],"interaction":[6],"with":[7,58,162],"the":[8,70,137,167],"world":[9,22,61,117],"at":[10,80],"test":[11,81],"time,":[12],"they":[13],"must":[14],"be":[15],"able":[16],"explore":[18],"effectively,":[19],"acquire":[20],"new":[21],"knowledge":[23,118],"and":[24,30,63,94,123,128,192],"skills,":[25],"retain":[26],"relevant":[27],"episodic":[28,120],"experiences,":[29],"plan":[31],"over":[32],"long":[33],"horizons.":[34],"To":[35],"evaluate":[36,132],"these":[37],"key":[38,148],"abilities":[39],"of":[40,197],"test-time":[41,199],"continual":[42],"learning":[43,73,76,93],"agents,":[44],"we":[45,183],"introduce":[46],"AgentOdyssey,":[47],"a":[48,87,101],"novel":[49],"evaluation":[50,103],"framework":[51],"that":[52,75,91,105,154,185],"procedurally":[53],"generates":[54],"open-ended":[55],"text":[56],"games":[57],"rich":[59],"entities,":[60],"dynamics,":[62],"long-horizon":[64,89],"tasks.":[65],"Critically,":[66],"AgentOdyssey":[67],"goes":[68],"beyond":[69],"conventional":[71],"machine":[72],"assumption":[74],"does":[77],"not":[78,107],"occur":[79],"time":[82],"by":[83],"placing":[84],"in":[86,136,146],"continuous,":[88],"setting":[90],"interleaves":[92],"inference":[95],"throughout":[96],"deployment.":[97],"We":[98,131],"further":[99],"propose":[100],"multifaceted":[102],"methodology":[104],"measures":[106],"only":[108],"game":[109],"progress":[110],"but":[111],"also":[112],"offers":[113],"diagnostic":[114],"tests":[115],"on":[116],"acquisition,":[119],"memory,":[121],"object":[122],"action":[124,126],"exploration,":[125],"diversity,":[127],"model":[129],"cost.":[130],"diverse":[133],"agent":[134,169,181,190,198],"paradigms":[135,191],"generated":[138],"games.":[139],"Our":[140],"experimental":[141],"results":[142],"reveal":[143],"critical":[144],"limits":[145],"agents'":[147],"abilities,":[149],"as":[150,152],"well":[151],"factors":[153],"influence":[155],"their":[156],"meaningful":[157],"horizon.":[158],"Although":[159],"performance":[160],"scales":[161],"stronger":[163],"base":[164],"models,":[165],"even":[166],"top":[168],"remains":[170],"far":[171],"below":[172],"human":[173],"performance,":[174],"leaving":[175],"substantial":[176],"headroom":[177],"for":[178],"improvement.":[179],"Among":[180],"mechanisms,":[182],"find":[184],"short-term":[186],"memory":[187],"benefits":[188],"multiple":[189],"is":[193],"an":[194],"important":[195],"component":[196],"training.":[200]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-26T00:00:00"}