{"id":"https://openalex.org/W6967091053","doi":"https://doi.org/10.48550/arxiv.2507.08828","title":"Recurrent Expansion: A Pathway Toward the Next Generation of Deep Learning","display_name":"Recurrent Expansion: A Pathway Toward the Next Generation of Deep Learning","publication_year":2025,"publication_date":"2025-07-04","ids":{"openalex":"https://openalex.org/W6967091053","doi":"https://doi.org/10.48550/arxiv.2507.08828"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2507.08828","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.08828","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2507.08828","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Berghout, Tarek","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Berghout, Tarek","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.14980000257492065,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.14980000257492065,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.10559999942779541,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.10429999977350235,"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/deep-learning","display_name":"Deep learning","score":0.748199999332428},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6283000111579895},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.522599995136261},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4878999888896942},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.42489999532699585},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3878999948501587},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.38769999146461487},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.36340001225471497}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7592999935150146},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7498999834060669},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.748199999332428},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6283000111579895},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.522599995136261},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4878999888896942},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4381999969482422},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.42489999532699585},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3878999948501587},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.38769999146461487},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.36340001225471497},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.35899999737739563},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3192000091075897},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3107999861240387},{"id":"https://openalex.org/C40506919","wikidata":"https://www.wikidata.org/wiki/Q7452469","display_name":"Sequence learning","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.29350000619888306},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C52970973","wikidata":"https://www.wikidata.org/wiki/Q2497134","display_name":"Adaptive system","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.27149999141693115}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2507.08828","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.08828","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2507.08828","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2507.08828","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":"article"},"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":{"This":[0],"paper":[1],"introduces":[2,125],"Recurrent":[3],"Expansion":[4],"(RE)":[5],"as":[6,69],"a":[7,137,154,169],"new":[8,155],"learning":[9,26,36,144,166],"paradigm":[10],"that":[11],"advances":[12],"beyond":[13],"conventional":[14],"Machine":[15],"Learning":[16,20],"(ML)":[17],"and":[18,54,105,121,128,174],"Deep":[19],"(DL).":[21],"While":[22],"DL":[23],"focuses":[24],"on":[25],"from":[27,37,87,101,141],"static":[28],"data":[29,49],"representations,":[30],"RE":[31,44,76,96,135],"proposes":[32],"an":[33],"additional":[34],"dimension:":[35],"the":[38,151],"evolving":[39],"behavior":[40],"of":[41,48,113,157,161,196],"models":[42,112,159],"themselves.":[43],"emphasizes":[45],"multiple":[46],"mappings":[47],"through":[50,94,107],"identical":[51],"deep":[52],"architectures":[53,115],"analyzes":[55],"their":[56,164,187],"internal":[57],"representations":[58],"(i.e.,":[59],"feature":[60],"maps)":[61],"in":[62,139,185,192],"conjunction":[63],"with":[64],"observed":[65],"performance":[66],"signals":[67,100],"such":[68],"loss.":[70],"By":[71],"incorporating":[72],"these":[73],"behavioral":[74],"traces,":[75],"enables":[77],"iterative":[78],"self-improvement,":[79],"allowing":[80],"each":[81],"model":[82,103],"version":[83],"to":[84,145,182],"gain":[85],"insight":[86],"its":[88],"predecessors.":[89],"The":[90],"framework":[91],"is":[92,190],"extended":[93],"Multiverse":[95],"(MVRE),":[97],"which":[98],"aggregates":[99],"parallel":[102],"instances,":[104],"further":[106],"Heterogeneous":[108],"MVRE":[109],"(HMVRE),":[110],"where":[111],"varying":[114],"contribute":[116],"diverse":[117],"perspectives.":[118],"A":[119,178],"scalable":[120],"adaptive":[122,175],"variant,":[123],"Sc-HMVRE,":[124],"selective":[126],"mechanisms":[127],"scale":[129],"diversity":[130],"for":[131,153],"real-world":[132],"deployment.":[133],"Altogether,":[134],"presents":[136],"shift":[138],"DL:":[140],"purely":[142],"representational":[143],"behavior-aware,":[146],"self-evolving":[147],"systems.":[148],"It":[149],"lays":[150],"groundwork":[152],"class":[156],"intelligent":[158],"capable":[160],"reasoning":[162],"over":[163],"own":[165,188],"dynamics,":[167],"offering":[168],"path":[170],"toward":[171],"scalable,":[172],"introspective,":[173],"artificial":[176],"intelligence.":[177],"simple":[179],"code":[180],"example":[181],"support":[183],"beginners":[184],"running":[186],"experiments":[189],"provided":[191],"Code":[193],"Availability":[194],"Section":[195],"this":[197],"paper.":[198]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}