{"id":"https://openalex.org/W7139102465","doi":"https://doi.org/10.48550/arxiv.2603.17070","title":"Large Reasoning Models Struggle to Transfer Parametric Knowledge Across Scripts","display_name":"Large Reasoning Models Struggle to Transfer Parametric Knowledge Across Scripts","publication_year":2026,"publication_date":"2026-03-17","ids":{"openalex":"https://openalex.org/W7139102465","doi":"https://doi.org/10.48550/arxiv.2603.17070"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.17070","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17070","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":null,"license_id":null,"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.2603.17070","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129888844","display_name":"Lucas Bandarkar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bandarkar, Lucas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129958065","display_name":"Alan Ansell","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ansell, Alan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130054390","display_name":"Trevor Cohn","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cohn, Trevor","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/T10028","display_name":"Topic Modeling","score":0.6223000288009644,"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/T10028","display_name":"Topic Modeling","score":0.6223000288009644,"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":0.05550000071525574,"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":0.05469999834895134,"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/pipeline","display_name":"Pipeline (software)","score":0.5644000172615051},{"id":"https://openalex.org/keywords/scripting-language","display_name":"Scripting language","score":0.5421000123023987},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.49630001187324524},{"id":"https://openalex.org/keywords/knowledge-transfer","display_name":"Knowledge transfer","score":0.49480000138282776},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.428600013256073},{"id":"https://openalex.org/keywords/automated-reasoning","display_name":"Automated reasoning","score":0.42340001463890076},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.3741999864578247},{"id":"https://openalex.org/keywords/knowledge-extraction","display_name":"Knowledge extraction","score":0.3488999903202057}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6909999847412109},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5644000172615051},{"id":"https://openalex.org/C61423126","wikidata":"https://www.wikidata.org/wiki/Q187432","display_name":"Scripting language","level":2,"score":0.5421000123023987},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5338000059127808},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.49480000138282776},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.428600013256073},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.42340001463890076},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41690000891685486},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.3488999903202057},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3433000147342682},{"id":"https://openalex.org/C2777220311","wikidata":"https://www.wikidata.org/wiki/Q6423340","display_name":"Knowledge acquisition","level":2,"score":0.3158000111579895},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.30239999294281006},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.29670000076293945},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.2955000102519989},{"id":"https://openalex.org/C2776291881","wikidata":"https://www.wikidata.org/wiki/Q6423378","display_name":"Knowledge level","level":2,"score":0.2922999858856201},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C24574437","wikidata":"https://www.wikidata.org/wiki/Q7135228","display_name":"Parametric model","level":3,"score":0.2768000066280365},{"id":"https://openalex.org/C84685590","wikidata":"https://www.wikidata.org/wiki/Q1540472","display_name":"Knowledge engineering","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.2685999870300293},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.2662999927997589}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.17070","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17070","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.17070","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.17070","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.829868733882904,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1,87,107],"work,":[2],"we":[3,30,127,172],"analyze":[4],"shortcomings":[5],"in":[6,10,21,100],"cross-lingual":[7,180],"knowledge":[8,22,47,73,152,182],"transfer":[9,23,74,167,183],"large,":[11],"modern":[12],"reasoning":[13,120],"LLMs.":[14],"We":[15,85,112,155],"demonstrate":[16],"that":[17,59,106,115,157,174],"the":[18,37,50,69,91,94,98,139,165],"perceived":[19],"gap":[20],"is":[24,68,176],"primarily":[25],"a":[26,129,170],"script":[27,60],"barrier.":[28],"First,":[29],"conduct":[31],"an":[32],"observational":[33],"data":[34],"analysis":[35,57],"on":[36,42],"performance":[38],"of":[39,72,97],"thinking":[40],"models":[41,160],"two":[43,159],"datasets":[44],"with":[45,93],"local":[46],"from":[48],"around":[49],"world,":[51],"ECLeKTic":[52],"and":[53,79,104],"MultiLoKo.":[54],"Our":[55],"regression":[56],"shows":[58],"match":[61],"-":[62,67],"not":[63],"language":[64,103],"or":[65],"family":[66],"primary":[70],"predictor":[71],"failure":[75],"once":[76],"model":[77,140],"capability":[78],"question":[80],"difficulty":[81],"are":[82],"accounted":[83],"for.":[84],"further":[86],"finding":[88],"by":[89],"providing":[90],"LLMs":[92,117],"key":[95],"entities":[96],"questions":[99],"their":[101],"source":[102],"find":[105],"disproportionately":[108],"improves":[109],"cross-script":[110,166],"questions.":[111],"then":[113],"posit":[114],"these":[116],"could":[118],"be":[119],"better":[121,142,163],"at":[122,153],"test-time.":[123],"To":[124],"evaluate":[125],"this,":[126],"develop":[128],"synthetic":[130],"generation":[131],"pipeline":[132],"to":[133,137,141,149,161,178],"design":[134],"SFT":[135],"samples":[136],"encourage":[138],"reason":[143,162],"about":[144],"transliteration":[145],"ambiguities":[146],"when":[147],"trying":[148],"fetch":[150],"parametric":[151,181],"inference-time.":[154],"show":[156],"teaching":[158],"reduces":[164],"gap.":[168],"As":[169],"result,":[171],"conclude":[173],"there":[175],"potential":[177],"improve":[179],"during":[184],"post-training.":[185]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-03-20T00:00:00"}
