{"id":"https://openalex.org/W7163126962","doi":"https://doi.org/10.48550/arxiv.2606.00732","title":"SHARP: Sleep-based Hierarchical Accelerated Replay for Long Range Non-Stationary Temporal Pattern Recognition","display_name":"SHARP: Sleep-based Hierarchical Accelerated Replay for Long Range Non-Stationary Temporal Pattern Recognition","publication_year":2026,"publication_date":"2026-05-30","ids":{"openalex":"https://openalex.org/W7163126962","doi":"https://doi.org/10.48550/arxiv.2606.00732"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.00732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00732","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":"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.2606.00732","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137621460","display_name":"Jayanta Dey","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dey, Jayanta","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102984658","display_name":"Shikhar Srivastava","orcid":"https://orcid.org/0000-0002-0120-4369"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Srivastava, Shikhar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044341747","display_name":"Itamar Lerner","orcid":"https://orcid.org/0000-0003-2525-2741"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lerner, Itamar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046979072","display_name":"Christopher Kanan","orcid":"https://orcid.org/0000-0002-6412-995X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kanan, Christopher","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5067236813","display_name":"Dhireesha Kudithipudi","orcid":"https://orcid.org/0000-0003-4462-5224"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kudithipudi, Dhireesha","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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.35269999504089355,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.35269999504089355,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10985","display_name":"Sleep and Wakefulness Research","score":0.20180000364780426,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.08529999852180481,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/backpropagation","display_name":"Backpropagation","score":0.6834999918937683},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6794999837875366},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6776999831199646},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5462999939918518},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5157999992370605},{"id":"https://openalex.org/keywords/sequence-learning","display_name":"Sequence learning","score":0.5005999803543091},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.45419999957084656},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4496999979019165},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4490000009536743},{"id":"https://openalex.org/keywords/data-stream","display_name":"Data stream","score":0.43810001015663147}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7773000001907349},{"id":"https://openalex.org/C155032097","wikidata":"https://www.wikidata.org/wiki/Q798503","display_name":"Backpropagation","level":3,"score":0.6834999918937683},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6794999837875366},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6776999831199646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.609000027179718},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5462999939918518},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5157999992370605},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.5005999803543091},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4496999979019165},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4490000009536743},{"id":"https://openalex.org/C2778484313","wikidata":"https://www.wikidata.org/wiki/Q1172540","display_name":"Data stream","level":2,"score":0.43810001015663147},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.414900004863739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40119999647140503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38670000433921814},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.37450000643730164},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.32679998874664307},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C2780490138","wikidata":"https://www.wikidata.org/wiki/Q7079636","display_name":"Offline learning","level":3,"score":0.28679999709129333},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.2847999930381775},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C2780150128","wikidata":"https://www.wikidata.org/wiki/Q21948731","display_name":"Extreme learning machine","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C135796866","wikidata":"https://www.wikidata.org/wiki/Q7315328","display_name":"Reservoir computing","level":4,"score":0.2619999945163727},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.00732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00732","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":"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.2606.00732","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.00732","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":"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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Learning":[0],"long-range":[1,127,165],"non-stationary":[2,113],"temporal":[3,79,236],"patterns":[4],"remains":[5],"a":[6,29,75,85,90,97],"core":[7],"challenge":[8],"for":[9,59,119,126],"modern":[10],"sequence":[11],"models,":[12],"particularly":[13],"in":[14,28,136,146,154],"strict":[15],"streaming":[16],"settings.":[17],"In":[18,183],"these":[19,66],"settings,":[20],"data":[21,207],"arrive":[22],"sequentially":[23],"and":[24,43,96,109,158,171,189,216],"must":[25],"be":[26],"processed":[27],"single":[30],"pass":[31],"without":[32],"simultaneously":[33],"revisiting":[34],"past":[35,94],"observations.":[36],"Standard":[37],"architectures,":[38],"including":[39],"recurrent":[40,197],"neural":[41],"networks":[42],"transformers,":[44],"are":[45,152,224],"constrained":[46],"by":[47,115,131,199,226],"either":[48],"truncated":[49],"backpropagation":[50,120],"through":[51,121],"time":[52,122],"horizon":[53],"or":[54],"explicit":[55],"input":[56],"window":[57],"length":[58],"long":[60],"range":[61],"credit":[62,128],"assignment.":[63,129],"To":[64],"address":[65],"limitations,":[67],"we":[68,174,191],"propose":[69],"SHARP":[70,141,194],"(Sleep-based":[71],"Hierarchical":[72],"Accelerated":[73],"Replay),":[74],"framework":[76],"that":[77,88,100,193],"decomposes":[78],"learning":[80],"into":[81,160],"two":[82],"complementary":[83],"components:":[84],"memory":[86,150,162],"module":[87,99],"accumulates":[89],"structured":[91,149],"history":[92],"of":[93,179],"inputs,":[95],"pattern-recognition":[98],"operates":[101],"over":[102,196],"this":[103],"memory.":[104],"This":[105],"separation":[106],"enables":[107],"resource-":[108],"compute-efficient":[110],"adaptation":[111],"to":[112,210,218],"dynamics":[114],"eliminating":[116],"the":[117,132,176,180,213],"need":[118],"across":[123],"many":[124],"steps":[125],"Inspired":[130],"accelerated":[133,156],"replay":[134],"observed":[135],"rodents":[137],"during":[138],"slow-wave":[139],"sleep,":[140],"incorporates":[142],"offline":[143],"(sleep)":[144],"phases":[145],"which":[147,230],"temporally":[148],"traces":[151],"replayed":[153],"an":[155,232],"form":[157],"integrated":[159],"higher-level":[161],"representations,":[163],"improving":[164],"context":[166,237],"retention.":[167],"Through":[168],"controlled":[169],"simulations":[170],"ablation":[172],"studies,":[173],"characterize":[175],"key":[177],"properties":[178],"proposed":[181],"framework.":[182],"benchmark":[184],"datasets":[185],"such":[186],"as":[187],"text8":[188],"PG-19,":[190],"demonstrate":[192],"improves":[195],"baselines":[198],"retaining":[200],"next-token":[201],"predictive":[202],"performance":[203],"on":[204],"previously":[205],"seen":[206],"while":[208],"continuing":[209],"learn":[211],"from":[212],"current":[214],"stream":[215],"generalizing":[217],"future":[219],"unseen":[220],"data.":[221],"These":[222],"gains":[223],"enabled":[225],"its":[227],"hierarchical":[228],"structure,":[229],"yields":[231],"exponentially":[233],"increasing":[234],"effective":[235],"with":[238],"only":[239],"linear-time":[240],"computational":[241],"cost.":[242]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-03T00:00:00"}
