{"id":"https://openalex.org/W7161948647","doi":"https://doi.org/10.48550/arxiv.2605.20201","title":"Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning","display_name":"Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning","publication_year":2026,"publication_date":"2026-04-06","ids":{"openalex":"https://openalex.org/W7161948647","doi":"https://doi.org/10.48550/arxiv.2605.20201"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.20201","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20201","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.2605.20201","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136653056","display_name":"Miao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Miao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000050905","display_name":"Irina Saparina","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saparina, Irina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057822982","display_name":"Alexander Gurung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gurung, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136650432","display_name":"Mirella Lapata","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lapata, Mirella","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":0.46140000224113464,"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":0.46140000224113464,"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":0.20569999516010284,"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":0.11500000208616257,"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/proxy","display_name":"Proxy (statistics)","score":0.7001000046730042},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5406000018119812},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.39739999175071716},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.3944000005722046},{"id":"https://openalex.org/keywords/model-based-reasoning","display_name":"Model-based reasoning","score":0.30320000648498535},{"id":"https://openalex.org/keywords/non-monotonic-logic","display_name":"Non-monotonic logic","score":0.2919999957084656},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.29100000858306885}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.734000027179718},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.7001000046730042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6036999821662903},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5479999780654907},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5406000018119812},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.39739999175071716},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.3944000005722046},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.30320000648498535},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.29100000858306885},{"id":"https://openalex.org/C86827895","wikidata":"https://www.wikidata.org/wiki/Q7098582","display_name":"Opportunistic reasoning","level":4,"score":0.28839999437332153},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2750999927520752},{"id":"https://openalex.org/C83725634","wikidata":"https://www.wikidata.org/wiki/Q7268699","display_name":"Qualitative reasoning","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2531999945640564},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.20201","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20201","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.2605.20201","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.20201","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":[{"score":0.6551840305328369,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"large":[1],"language":[2],"models":[3,52,136],"support":[4],"inputs":[5],"of":[6,32],"up":[7],"to":[8,82,145],"10":[9],"million":[10],"tokens,":[11],"yet":[12],"they":[13],"perform":[14],"poorly":[15],"on":[16,94],"long-context":[17,65,142],"tasks":[18,24],"that":[19,74,125],"require":[20],"complex":[21],"reasoning.":[22],"Such":[23],"can":[25],"be":[26],"solved":[27],"using":[28],"only":[29],"a":[30,36,54,70,103],"subset":[31],"the":[33,42,47,110],"input":[34],"--":[35,39],"proxy":[37,59,80,95],"context":[38],"rather":[40],"than":[41],"full":[43,61,83,114],"sequence.":[44],"Despite":[45],"sharing":[46],"same":[48],"underlying":[49],"reasoning":[50,76,92,143],"process,":[51],"exhibit":[53],"significant":[55],"performance":[56],"disparity":[57],"between":[58],"and":[60,107],"contexts.":[62,85],"To":[63],"improve":[64],"reasoning,":[66],"we":[67,87],"propose":[68],"ProxyCoT,":[69],"novel":[71],"training":[72],"framework":[73],"transfers":[75],"capabilities":[77,144],"from":[78,102],"short":[79],"contexts":[81,96,116],"long":[84,115],"Specifically,":[86],"first":[88],"obtain":[89],"high-quality":[90],"chain-of-thought":[91],"traces":[93,112],"through":[97],"reinforcement":[98],"learning":[99],"or":[100],"distillation":[101],"larger":[104],"teacher":[105],"model,":[106],"then":[108],"ground":[109],"generated":[111],"in":[113],"with":[117,131,138],"supervised":[118],"fine-tuning.":[119],"Experiments":[120],"across":[121],"different":[122],"datasets":[123],"demonstrate":[124],"ProxyCoT":[126,139],"consistently":[127],"outperforms":[128],"strong":[129],"baselines":[130],"reduced":[132],"computational":[133],"overhead.":[134],"Furthermore,":[135],"trained":[137],"generalize":[140],"their":[141],"out-of-domain":[146],"tasks.":[147]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-22T00:00:00"}