{"id":"https://openalex.org/W7166550797","doi":"https://doi.org/10.48550/arxiv.2606.27806","title":"Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents","display_name":"Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents","publication_year":2026,"publication_date":"2026-06-26","ids":{"openalex":"https://openalex.org/W7166550797","doi":"https://doi.org/10.48550/arxiv.2606.27806"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.27806","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27806","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.2606.27806","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139549129","display_name":"Xinyuan Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Xinyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125930762","display_name":"Zekun Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Zekun","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":0.28360000252723694,"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":0.28360000252723694,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.09650000184774399,"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":0.0957999974489212,"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/hallucinating","display_name":"Hallucinating","score":0.7179999947547913},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.6758000254631042},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4936000108718872},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.47209998965263367},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.47119998931884766},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.46399998664855957}],"concepts":[{"id":"https://openalex.org/C2911011789","wikidata":"https://www.wikidata.org/wiki/Q130741","display_name":"Hallucinating","level":2,"score":0.7179999947547913},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.6758000254631042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6294000148773193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5246000289916992},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4936000108718872},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.489300012588501},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.47209998965263367},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.47119998931884766},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.46399998664855957},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44999998807907104},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4259999990463257},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35010001063346863},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.33180001378059387},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2897999882698059},{"id":"https://openalex.org/C79897977","wikidata":"https://www.wikidata.org/wiki/Q5054568","display_name":"Causal chain","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C2781256819","wikidata":"https://www.wikidata.org/wiki/Q16828835","display_name":"Antecedent (behavioral psychology)","level":2,"score":0.2687999904155731}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.27806","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27806","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.2606.27806","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.27806","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Language":[0],"agents":[1,23],"plan":[2],"by":[3],"generating":[4],"not":[5],"only":[6,131],"actions":[7],"but":[8,25,55],"also":[9,27],"implicit":[10],"predictions":[11],"of":[12],"how":[13],"the":[14,93,97,119,124],"world":[15,49],"will":[16],"change.":[17],"These":[18,174],"imagined":[19,121],"state":[20,34,110,145],"updates":[21],"make":[22],"flexible,":[24],"they":[26],"create":[28],"a":[29,101],"distinct":[30],"failure":[31],"mode:":[32],"hallucinated":[33,144],"claims":[35],"can":[36,182],"be":[37],"written":[38],"into":[39],"context":[40],"and":[41,69,80,113,127],"propagated":[42],"across":[43],"subsequent":[44],"decisions.":[45],"In":[46,147],"contrast,":[47],"parametric":[48,103,125,179],"models":[50,181],"provide":[51],"measurable":[52],"transition":[53,104,180],"errors":[54],"are":[56],"often":[57],"weaker":[58],"semantic":[59,193],"planners.":[60],"We":[61,84],"study":[62],"this":[63],"tradeoff":[64],"in":[65,159],"graph-structured":[66,136],"planning":[67,137,190],"environments":[68],"introduce":[70],"metrics":[71],"for":[72,188],"agent-world-model":[73],"error,":[74],"including":[75],"hallucinated-state":[76,153],"rate,":[77],"propagation":[78],"depth,":[79],"long-horizon":[81],"error":[82],"growth.":[83],"then":[85],"propose":[86],"Hybrid":[87],"World-Model":[88],"Planning":[89],"(Hybrid-WM),":[90],"which":[91],"keeps":[92],"language":[94],"model":[95,105],"as":[96,184],"planner":[98],"while":[99,142],"using":[100],"small":[102],"to":[106,157,168],"predict":[107],"action":[108],"validity,":[109],"deltas,":[111],"risk,":[112],"value.":[114],"A":[115],"consistency":[116],"gate":[117],"compares":[118],"agent's":[120],"delta":[122],"with":[123,170],"prediction":[126],"triggers":[128],"targeted":[129],"revision":[130],"under":[132],"disagreement.":[133],"Across":[134],"four":[135],"benchmarks,":[138],"Hybrid-WM":[139],"improves":[140,164],"success":[141,165],"reducing":[143],"propagation.":[146],"live":[148],"GPT-4o-mini":[149],"evaluations,":[150],"it":[151,163],"reduces":[152],"rate":[154],"from":[155,166],"0.176":[156],"0.035;":[158],"calibrated":[160],"simulator":[161],"ablations,":[162],"0.668":[167],"0.838":[169],"modest":[171],"additional":[172],"inference.":[173],"results":[175],"suggest":[176],"that":[177],"lightweight":[178],"serve":[183],"effective":[185],"grounding":[186],"mechanisms":[187],"language-agent":[189],"without":[191],"replacing":[192],"reasoning.":[194]},"counts_by_year":[],"updated_date":"2026-07-08T06:17:01.165560","created_date":"2026-06-30T00:00:00"}
