{"id":"https://openalex.org/W7130540859","doi":"https://doi.org/10.48550/arxiv.2602.15902","title":"Doc-to-LoRA: Learning to Instantly Internalize Contexts","display_name":"Doc-to-LoRA: Learning to Instantly Internalize Contexts","publication_year":2026,"publication_date":"2026-02-13","ids":{"openalex":"https://openalex.org/W7130540859","doi":"https://doi.org/10.48550/arxiv.2602.15902"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.15902","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062616009","display_name":"Rujikorn Charakorn","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Charakorn, Rujikorn","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005809555","display_name":"Edoardo Cetin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cetin, Edoardo","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126413963","display_name":"Shinnosuke Uesaka","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Uesaka, Shinnosuke","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126398793","display_name":"Robert Tjarko Lange","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lange, Robert Tjarko","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5062616009"],"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":0.48969998955726624,"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":0.48969998955726624,"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":0.10930000245571136,"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/T13629","display_name":"Text Readability and Simplification","score":0.04479999840259552,"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/inference","display_name":"Inference","score":0.7512000203132629},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5494999885559082},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4916999936103821},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.45570001006126404},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.3540000021457672},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.32829999923706055},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.32170000672340393},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.3172999918460846},{"id":"https://openalex.org/keywords/adapter","display_name":"Adapter (computing)","score":0.3172000050544739}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7893000245094299},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7512000203132629},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5494999885559082},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5335000157356262},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4916999936103821},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48429998755455017},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.45570001006126404},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3540000021457672},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3172999918460846},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.3172000050544739},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3116999864578247},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.28619998693466187},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.27549999952316284},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C88626702","wikidata":"https://www.wikidata.org/wiki/Q1128903","display_name":"Continuation","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C61641136","wikidata":"https://www.wikidata.org/wiki/Q1107019","display_name":"Cognitive load","level":3,"score":0.2662999927997589},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26499998569488525},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2614000141620636},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.2513999938964844},{"id":"https://openalex.org/C12186640","wikidata":"https://www.wikidata.org/wiki/Q6815743","display_name":"Memory model","level":3,"score":0.25}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.15902","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.15902","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15902","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.15902","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.49883145093917847,"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":{"Long":[0],"input":[1],"sequences":[2],"are":[3],"central":[4],"to":[5,45,63,88,116],"in-context":[6],"learning,":[7],"document":[8],"understanding,":[9],"and":[10,28,48,98,161,182],"multi-step":[11],"reasoning":[12],"of":[13,23,104,172,178],"Large":[14],"Language":[15],"Models":[16],"(LLMs).":[17],"However,":[18],"the":[19,93,105,123,134,176],"quadratic":[20],"attention":[21],"cost":[22],"Transformers":[24],"makes":[25],"inference":[26,103],"memory-intensive":[27],"slow.":[29],"While":[30],"context":[31,138],"distillation":[32,41],"(CD)":[33],"can":[34,168],"transfer":[35],"information":[36],"into":[37,119],"model":[38],"parameters,":[39],"per-prompt":[40],"is":[42],"impractical":[43],"due":[44],"training":[46],"costs":[47],"latency.":[49,163],"To":[50],"address":[51],"these":[52],"limitations,":[53],"we":[54],"propose":[55],"Doc-to-LoRA":[56],"(D2L),":[57],"a":[58,68,78,82,109],"lightweight":[59],"hypernetwork":[60],"that":[61,121,166],"meta-learns":[62],"perform":[64],"approximate":[65],"CD":[66,154],"within":[67],"single":[69],"forward":[70],"pass.":[71],"Given":[72],"an":[73],"unseen":[74],"prompt,":[75],"D2L":[76,113,151,167],"generates":[77],"LoRA":[79],"adapter":[80],"for":[81],"target":[83,106,135],"LLM,":[84],"enabling":[85],"subsequent":[86],"queries":[87],"be":[89],"answered":[90],"without":[91],"re-consuming":[92],"original":[94],"context,":[95],"reducing":[96,157],"latency":[97],"KV-cache":[99],"memory":[100,159],"consumption":[101,160],"during":[102],"LLM.":[107],"On":[108,144],"long-context":[110],"needle-in-a-haystack":[111],"task,":[112],"successfully":[114],"learns":[115],"map":[117],"contexts":[118],"adapters":[120],"store":[122],"needle":[124],"information,":[125],"achieving":[126],"near-perfect":[127],"zero-shot":[128],"accuracy":[129],"at":[130],"sequence":[131],"lengths":[132],"exceeding":[133],"LLM's":[136],"native":[137],"window":[139],"by":[140],"more":[141],"than":[142],"4x.":[143],"real-world":[145],"QA":[146],"datasets":[147],"with":[148],"limited":[149],"compute,":[150],"outperforms":[152],"standard":[153],"while":[155],"significantly":[156],"peak":[158],"update":[162],"We":[164],"envision":[165],"facilitate":[169],"rapid":[170],"adaptation":[171],"LLMs,":[173],"opening":[174],"up":[175],"possibility":[177],"frequent":[179],"knowledge":[180],"updates":[181],"personalized":[183],"chat":[184],"behavior.":[185]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-20T00:00:00"}
