{"id":"https://openalex.org/W7155042935","doi":"https://doi.org/10.48550/arxiv.2604.17377","title":"AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models","display_name":"AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models","publication_year":2026,"publication_date":"2026-04-19","ids":{"openalex":"https://openalex.org/W7155042935","doi":"https://doi.org/10.48550/arxiv.2604.17377"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.17377","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17377","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":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.17377","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134104552","display_name":"Zhanyu Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Shen, Zhanyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134204775","display_name":"Sijie Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Sijie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134167404","display_name":"Zhicheng Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Zhicheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134121894","display_name":"Weiqin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Weiqin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134166196","display_name":"Yile Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yile","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134141693","display_name":"Hui Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Hui","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5134104552"],"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":0.36309999227523804,"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":0.36309999227523804,"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":0.21400000154972076,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.05700000002980232,"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/rewriting","display_name":"Rewriting","score":0.6678000092506409},{"id":"https://openalex.org/keywords/automatic-summarization","display_name":"Automatic summarization","score":0.5986999869346619},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5248000025749207},{"id":"https://openalex.org/keywords/associative-property","display_name":"Associative property","score":0.4844000041484833},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4837999939918518},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.45399999618530273},{"id":"https://openalex.org/keywords/narrative","display_name":"Narrative","score":0.43700000643730164},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.3781999945640564},{"id":"https://openalex.org/keywords/language-construct","display_name":"Language construct","score":0.37700000405311584}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8055999875068665},{"id":"https://openalex.org/C154690210","wikidata":"https://www.wikidata.org/wiki/Q1668499","display_name":"Rewriting","level":2,"score":0.6678000092506409},{"id":"https://openalex.org/C170858558","wikidata":"https://www.wikidata.org/wiki/Q1394144","display_name":"Automatic summarization","level":2,"score":0.5986999869346619},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5248000025749207},{"id":"https://openalex.org/C159423971","wikidata":"https://www.wikidata.org/wiki/Q177251","display_name":"Associative property","level":2,"score":0.4844000041484833},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4837999939918518},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45509999990463257},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.45399999618530273},{"id":"https://openalex.org/C199033989","wikidata":"https://www.wikidata.org/wiki/Q1318295","display_name":"Narrative","level":2,"score":0.43700000643730164},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40849998593330383},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C48859967","wikidata":"https://www.wikidata.org/wiki/Q6486712","display_name":"Language construct","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C188147891","wikidata":"https://www.wikidata.org/wiki/Q147638","display_name":"Cognitive science","level":1,"score":0.37130001187324524},{"id":"https://openalex.org/C88576662","wikidata":"https://www.wikidata.org/wiki/Q18646","display_name":"Episodic memory","level":3,"score":0.3422999978065491},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3359000086784363},{"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/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.322299987077713},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3199999928474426},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.31310001015663147},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.30730000138282776},{"id":"https://openalex.org/C71611378","wikidata":"https://www.wikidata.org/wiki/Q5165191","display_name":"Contextual design","level":3,"score":0.30480000376701355},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.2964000105857849},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C53442348","wikidata":"https://www.wikidata.org/wiki/Q745101","display_name":"Content-addressable memory","level":3,"score":0.2802000045776367},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2802000045776367},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C161407221","wikidata":"https://www.wikidata.org/wiki/Q4382939","display_name":"Cognitive model","level":3,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.17377","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17377","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":"doi:10.48550/arxiv.2604.17377","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17377","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":false,"raw_source_name":null,"raw_type":"article"},"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":{"While":[0],"large":[1],"language":[2],"models":[3,196],"have":[4],"achieved":[5],"remarkable":[6],"performance":[7],"in":[8,21,72],"complex":[9],"tasks,":[10],"they":[11],"still":[12],"need":[13],"a":[14,63,76,80,85],"memory":[15,25,65],"system":[16,156],"to":[17,103,159,164],"utilize":[18],"historical":[19],"experience":[20],"long-term":[22],"interactions.":[23,189],"Existing":[24],"methods":[26],"(e.g.,":[27],"A-Mem,":[28],"Mem0)":[29],"place":[30],"excessive":[31],"emphasis":[32],"on":[33,44,148,197],"organizing":[34],"interactions":[35],"by":[36,68],"frequently":[37],"rewriting":[38],"them,":[39],"however,":[40],"this":[41,58],"heavy":[42],"reliance":[43],"summarization":[45],"risks":[46],"diluting":[47],"essential":[48],"contextual":[49,186],"nuances":[50],"and":[51,162,177,194],"obscuring":[52],"key":[53],"retrieval":[54,90,106,183],"features.":[55],"To":[56,117],"bridge":[57],"gap,":[59],"we":[60,122],"introduce":[61],"AnchorMem,":[62],"novel":[64],"framework":[66],"inspired":[67],"the":[69,89,93,110,114,155,170,173,185,198],"Proust":[70],"Phenomenon":[71],"cognitive":[73],"science,":[74],"where":[75],"specific":[77,160],"anchor":[78],"triggers":[79],"holistic":[81],"recollection.":[82],"We":[83],"propose":[84],"method":[86,180],"that":[87,128,133,202],"decouples":[88],"unit":[91],"from":[92,100],"generation":[94],"context.":[95,116],"AnchorMem":[96,203],"extracts":[97],"atomic":[98],"facts":[99,138,161],"interaction":[101],"history":[102],"serve":[104],"as":[105,113,151],"anchors,":[107],"while":[108],"preserving":[109],"original":[111],"context":[112,171],"immutable":[115],"reveal":[118],"implicit":[119],"narrative":[120],"cues,":[121],"construct":[123],"an":[124],"associative":[125],"event":[126,131,141],"graph":[127],"uses":[129],"higher-order":[130],"links":[132],"bind":[134],"sets":[135],"of":[136,188],"related":[137],"into":[139],"shared":[140],"representations,":[142],"strengthening":[143],"cross-memory":[144],"integration":[145],"without":[146],"relying":[147],"generic":[149],"entities":[150],"bridges.":[152],"During":[153],"retrieval,":[154],"anchors":[157],"queries":[158],"events":[163],"locate":[165],"relevant":[166],"memories,":[167],"but":[168],"reconstructs":[169],"using":[172],"associated":[174],"raw":[175],"chunks":[176],"events.":[178],"Our":[179],"reconciles":[181],"fine-grained":[182],"with":[184],"integrity":[187],"Experiments":[190],"across":[191],"three":[192],"closed-source":[193],"open-source":[195],"LoCoMo":[199],"benchmark":[200],"demonstrate":[201],"significantly":[204],"outperforms":[205],"baselines.":[206],"Code":[207],"is":[208],"available":[209],"at":[210],"https://github.com/RayNeo-AI-2025/AnchorMem.":[211]},"counts_by_year":[],"updated_date":"2026-04-22T06:07:44.442478","created_date":"2026-04-22T00:00:00"}
