{"id":"https://openalex.org/W7161123472","doi":"https://doi.org/10.48550/arxiv.2605.13740","title":"Learning POMDP World Models from Observations with Language-Model Priors","display_name":"Learning POMDP World Models from Observations with Language-Model Priors","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161123472","doi":"https://doi.org/10.48550/arxiv.2605.13740"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13740","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.2605.13740","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120596128","display_name":"Valentin Six","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Six, Valentin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136113413","display_name":"Frederik Panse","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Panse, Frederik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136147968","display_name":"Mathis Fajeau","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fajeau, Mathis","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087108547","display_name":"Lancelot Da Costa","orcid":"https://orcid.org/0000-0003-0126-4588"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Da Costa, Lancelot","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130024024","display_name":"Mridul Sharma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sharma, Mridul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136099263","display_name":"Alfonso Amayuelas","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Amayuelas, Alfonso","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041315782","display_name":"Tim Z. Xiao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiao, Tim Z.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136172683","display_name":"David Hyland","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hyland, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022872779","display_name":"Philipp Hennig","orcid":"https://orcid.org/0000-0001-7293-6092"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hennig, Philipp","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136171347","display_name":"Bernhard Sch\u00f6lkopf","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sch\u00f6lkopf, Bernhard","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.2597000002861023,"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":0.2597000002861023,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.25600001215934753,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.14499999582767487,"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/partially-observable-markov-decision-process","display_name":"Partially observable Markov decision process","score":0.907800018787384},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.8417999744415283},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5023999810218811},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.414000004529953},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3944999873638153},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.3248000144958496},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.3212999999523163}],"concepts":[{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.907800018787384},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.8417999744415283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7328000068664551},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6310999989509583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.628000020980835},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5023999810218811},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.414000004529953},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.3212999999523163},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.289900004863739},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.28130000829696655},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.2800000011920929},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.2775999903678894},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2583000063896179}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13740","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.2605.13740","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13740","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":"Peace, Justice and strong institutions","score":0.5781168937683105,"id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Whether":[0],"navigating":[1],"a":[2,5,9,36,84,94,160,171],"building,":[3],"operating":[4],"robot,":[6],"or":[7],"playing":[8],"game,":[10],"an":[11,17,22,77],"agent":[12],"that":[13,27,115,136],"acts":[14],"effectively":[15],"in":[16,176],"environment":[18,28,58,150],"must":[19],"first":[20],"learn":[21],"internal":[23,42],"model":[24],"of":[25,110,129],"how":[26],"works.":[29],"Partially-observable":[30],"Markov":[31],"decision":[32],"processes":[33],"(POMDPs)":[34],"provide":[35],"flexible":[37],"modeling":[38],"class":[39],"for":[40,163],"such":[41],"world":[43],"models,":[44],"but":[45],"learning":[46,113,166],"them":[47,91],"from":[48,83],"observation-action":[49,86],"trajectories":[50,87],"alone":[51],"is":[52,151,180],"challenging":[53],"and":[54,73,88,107,142,170],"typically":[55],"requires":[56],"extensive":[57],"interaction.":[59],"We":[60],"ask":[61],"whether":[62],"language-model":[63,157],"priors":[64,158],"can":[65],"reduce":[66],"costly":[67],"interaction":[68],"by":[69],"leveraging":[70],"prior":[71],"knowledge,":[72],"introduce":[74],"\\emph{Pinductor}":[75,103],"(POMDP-inductor):":[76],"LLM":[78,140],"proposes":[79],"candidate":[80],"POMDP":[81,112,131],"models":[82],"few":[85],"iteratively":[89],"refines":[90],"to":[92,119],"optimize":[93],"belief-based":[95],"likelihood":[96],"score.":[97],"Despite":[98],"using":[99],"strictly":[100],"less":[101],"information,":[102],"matches":[104],"the":[105,120,126,149],"performance":[106,137],"sample":[108,127],"efficiency":[109,128],"LLM-based":[111],"methods":[114],"assume":[116],"privileged":[117],"access":[118],"hidden":[121],"state,":[122],"while":[123],"significantly":[124],"surpassing":[125],"tabular":[130],"baselines.":[132],"Further":[133],"results":[134,155],"show":[135],"scales":[138],"with":[139],"capability":[141],"degrades":[143],"gracefully":[144],"as":[145,159],"semantic":[146],"information":[147],"about":[148],"withheld.":[152],"Together,":[153],"these":[154],"position":[156],"practical":[161],"tool":[162],"sample-efficient":[164],"world-model":[165],"under":[167],"partial":[168],"observability,":[169],"step":[172],"toward":[173],"generalist":[174],"agents":[175],"real-world":[177],"environments.":[178],"Code":[179],"available":[181],"at":[182],"https://github.com/atomresearch/pinductor.":[183]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-15T00:00:00"}