{"id":"https://openalex.org/W6892017168","doi":"https://doi.org/10.48550/arxiv.2508.09874","title":"Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models","display_name":"Memory Decoder: A Pretrained, Plug-and-Play Memory for Large Language Models","publication_year":2025,"publication_date":"2025-08-13","ids":{"openalex":"https://openalex.org/W6892017168","doi":"https://doi.org/10.48550/arxiv.2508.09874"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2508.09874","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.09874","pdf_url":"https://arxiv.org/pdf/2508.09874","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2508.09874","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5143868418","display_name":"Jiaqi Cao","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Jiaqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126630212","display_name":"Jiarui Wang","orcid":"https://orcid.org/0009-0005-9890-2698","observed_orcids":["https://orcid.org/0009-0005-9890-2698"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Jiarui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5143898355","display_name":"Rubin Wei","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Rubin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Guo, Qipeng","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Qipeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Chen, Kai","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Zhou, Bowen","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Bowen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5024900991","display_name":"Zhouhan Lin","orcid":"https://orcid.org/0009-0009-7204-0689","observed_orcids":["https://orcid.org/0009-0009-7204-0689"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Zhouhan","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":true,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.5609999895095825,"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":0.5609999895095825,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.42579999566078186,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.004900000058114529,"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/large-language-models","display_name":"large language models","score":0.7829999923706055},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"domain adaptation","score":0.968999981880188},{"id":"https://openalex.org/keywords/retrieval-augmented-generation","display_name":"retrieval-augmented generation","score":0.7979999780654907},{"id":"https://openalex.org/keywords/catastrophic-forgetting","display_name":"catastrophic forgetting","score":0.9110000133514404},{"id":"https://openalex.org/keywords/specialized-domains","display_name":"specialized domains","score":0.4320000112056732},{"id":"https://openalex.org/keywords/biomedicine","display_name":"biomedicine","score":0.7590000033378601}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8474000096321106},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.6570000052452087},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6503999829292297},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.436599999666214},{"id":"https://openalex.org/C12186640","wikidata":"https://www.wikidata.org/wiki/Q6815743","display_name":"Memory model","level":3,"score":0.4325000047683716},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4090000092983246},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.39469999074935913},{"id":"https://openalex.org/C57863822","wikidata":"https://www.wikidata.org/wiki/Q905488","display_name":"Flat memory model","level":4,"score":0.3772999942302704},{"id":"https://openalex.org/C82687282","wikidata":"https://www.wikidata.org/wiki/Q66221","display_name":"Auxiliary memory","level":2,"score":0.3637999892234802},{"id":"https://openalex.org/C74426580","wikidata":"https://www.wikidata.org/wiki/Q719484","display_name":"Memory map","level":3,"score":0.36000001430511475},{"id":"https://openalex.org/C118702147","wikidata":"https://www.wikidata.org/wiki/Q189396","display_name":"Dynamic random-access memory","level":3,"score":0.35830000042915344},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3472999930381775},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.33660000562667847},{"id":"https://openalex.org/C171675096","wikidata":"https://www.wikidata.org/wiki/Q1143380","display_name":"Extended memory","level":4,"score":0.3334999978542328},{"id":"https://openalex.org/C2779602883","wikidata":"https://www.wikidata.org/wiki/Q15544750","display_name":"Memory architecture","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3000999987125397},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C39608478","wikidata":"https://www.wikidata.org/wiki/Q5015979","display_name":"Cache language model","level":5,"score":0.2903999984264374},{"id":"https://openalex.org/C76679254","wikidata":"https://www.wikidata.org/wiki/Q5165163","display_name":"Context-dependent memory","level":4,"score":0.28540000319480896},{"id":"https://openalex.org/C153247305","wikidata":"https://www.wikidata.org/wiki/Q835713","display_name":"Memory address","level":3,"score":0.2827000021934509},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.27459999918937683},{"id":"https://openalex.org/C98986596","wikidata":"https://www.wikidata.org/wiki/Q1143031","display_name":"Semiconductor memory","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.26159998774528503},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.25529998540878296},{"id":"https://openalex.org/C30390489","wikidata":"https://www.wikidata.org/wiki/Q4680748","display_name":"Adaptive memory","level":3,"score":0.25519999861717224},{"id":"https://openalex.org/C63511323","wikidata":"https://www.wikidata.org/wiki/Q908936","display_name":"Interleaved memory","level":4,"score":0.25200000405311584},{"id":"https://openalex.org/C87907426","wikidata":"https://www.wikidata.org/wiki/Q6815755","display_name":"Memory refresh","level":4,"score":0.2500999867916107}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2508.09874","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.09874","pdf_url":"https://arxiv.org/pdf/2508.09874","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2508.09874","doi":"https://doi.org/10.48550/arxiv.2508.09874","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2508.09874","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2508.09874","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2508.09874","pdf_url":"https://arxiv.org/pdf/2508.09874","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"score":0.5277585983276367,"display_name":"Peace, justice, and strong institutions","id":"https://metadata.un.org/sdg/16"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7171620560","https://openalex.org/W7204939093","https://openalex.org/W4401043762","https://openalex.org/W4385569751","https://openalex.org/W4417122032","https://openalex.org/W4414929045","https://openalex.org/W4321593827","https://openalex.org/W7129409960","https://openalex.org/W7140145563","https://openalex.org/W7136241682"],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"have":[4],"shown":[5],"strong":[6],"abilities":[7],"in":[8,169],"general":[9],"language":[10,102],"tasks,":[11],"yet":[12],"adapting":[13],"them":[14],"to":[15,46,82,128],"specific":[16],"domains":[17],"remains":[18],"a":[19,58,76,149,154,170],"challenge.":[20],"Current":[21],"method":[22],"like":[23],"Domain":[24],"Adaptive":[25],"Pretraining":[26],"(DAPT)":[27],"requires":[28],"costly":[29],"full-parameter":[30],"training":[31],"and":[32,50,125,135],"suffers":[33],"from":[34],"catastrophic":[35],"forgetting.":[36],"Meanwhile,":[37],"Retrieval-Augmented":[38],"Generation":[39],"(RAG)":[40],"introduces":[41,55,148],"substantial":[42],"inference":[43],"latency":[44],"due":[45],"expensive":[47],"nearest-neighbor":[48],"searches":[49],"longer":[51],"context.":[52],"This":[53,163],"paper":[54],"Memory":[56,73,93,117,146],"Decoder,":[57],"plug-and-play":[59,171],"pretrained":[60,101,156],"memory":[61,157,164],"that":[62,80,104,116],"enables":[63,119],"efficient":[64],"domain":[65],"adaptation":[66,121],"without":[67],"changing":[68],"the":[69,84,106,180],"original":[70],"model's":[71],"parameters.":[72],"Decoder":[74,94,118,147],"employs":[75],"small":[77],"transformer":[78],"decoder":[79],"learns":[81],"imitate":[83],"behavior":[85],"of":[86,122,142],"an":[87,140],"external":[88],"non-parametric":[89],"retriever.":[90],"Once":[91],"trained,":[92],"can":[95,166],"be":[96,167],"seamlessly":[97],"integrated":[98,168],"with":[99],"any":[100],"model":[103],"shares":[105],"same":[107],"tokenizer,":[108],"requiring":[109],"no":[110],"model-specific":[111],"modifications.":[112],"Experimental":[113],"results":[114],"demonstrate":[115],"effective":[120],"various":[123],"Qwen":[124],"Llama":[126],"models":[127,178],"three":[129],"distinct":[130],"specialized":[131],"domains:":[132],"biomedicine,":[133],"finance,":[134],"law,":[136],"reducing":[137],"perplexity":[138],"by":[139],"average":[141],"6.17":[143],"points.":[144],"Overall,":[145],"novel":[150],"paradigm":[151],"centered":[152],"on":[153],"specially":[155],"component":[158],"designed":[159],"for":[160],"domain-specific":[161],"adaptation.":[162],"architecture":[165],"manner,":[172],"consistently":[173],"enhancing":[174],"performance":[175],"across":[176],"multiple":[177],"within":[179],"target":[181],"domain.":[182]},"counts_by_year":[],"updated_date":"2026-10-31T12:09:11.096373","created_date":"2025-10-10T00:00:00"}
