{"id":"https://openalex.org/W7154170090","doi":"https://doi.org/10.48550/arxiv.2604.08556","title":"EMA Is Not All You Need: Mapping the Boundary Between Structure and Content in Recurrent Context","display_name":"EMA Is Not All You Need: Mapping the Boundary Between Structure and Content in Recurrent Context","publication_year":2026,"publication_date":"2026-03-17","ids":{"openalex":"https://openalex.org/W7154170090","doi":"https://doi.org/10.48550/arxiv.2604.08556"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.08556","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08556","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.2604.08556","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018244556","display_name":"Arth Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Singh, Arth","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5018244556"],"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":0.2786000072956085,"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":0.2786000072956085,"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":0.07590000331401825,"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/T12090","display_name":"Language and cultural evolution","score":0.0689999982714653,"subfield":{"id":"https://openalex.org/subfields/3316","display_name":"Cultural Studies"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6815000176429749},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.5026000142097473},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.4887000024318695},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4814999997615814},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.46230000257492065},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4293999969959259},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4138000011444092},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.40529999136924744},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40299999713897705}],"concepts":[{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6815000176429749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6100000143051147},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5458999872207642},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.5026000142097473},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.4887000024318695},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4814999997615814},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.46230000257492065},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4293999969959259},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4138000011444092},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.40529999136924744},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40299999713897705},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.38940000534057617},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.3853999972343445},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3776000142097473},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.3560999929904938},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.3271999955177307},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C111437709","wikidata":"https://www.wikidata.org/wiki/Q1277874","display_name":"Hebbian theory","level":3,"score":0.31850001215934753},{"id":"https://openalex.org/C2778152352","wikidata":"https://www.wikidata.org/wiki/Q5165061","display_name":"Content (measure theory)","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3091999888420105},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.29980000853538513},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2777000069618225},{"id":"https://openalex.org/C35639132","wikidata":"https://www.wikidata.org/wiki/Q7452468","display_name":"Sequence labeling","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C8521452","wikidata":"https://www.wikidata.org/wiki/Q203790","display_name":"Connectionism","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2572999894618988},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.08556","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08556","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.2604.08556","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.08556","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":{"What":[0],"exactly":[1],"do":[2],"efficient":[3],"sequence":[4],"models":[5],"gain":[6],"over":[7],"simple":[8],"temporal":[9,46],"averaging?":[10],"We":[11],"use":[12],"exponential":[13],"moving":[14],"average":[15],"(EMA)":[16],"traces,":[17],"the":[18,33,68,98,109,113,122,131],"simplest":[19],"recurrent":[20],"context":[21,86],"(no":[22],"gating,":[23],"no":[24,126],"content-based":[25],"retrieval),":[26],"as":[27],"a":[28,48,57,79,94],"controlled":[29],"probe":[30],"to":[31,112],"map":[32],"boundary":[34],"between":[35],"what":[36],"fixed-coefficient":[37],"accumulation":[38],"can":[39,129,150],"and":[40,93],"cannot":[41],"represent.":[42],"EMA":[43,74,85],"traces":[44,53,75,116],"encode":[45],"structure:":[47],"Hebbian":[49],"architecture":[50],"with":[51,64,101],"multi-timescale":[52],"achieves":[54],"96%":[55],"of":[56],"supervised":[58,69],"BiGRU":[59],"on":[60,71],"grammatical":[61],"role":[62],"assignment":[63],"zero":[65],"labels,":[66],"surpassing":[67],"model":[70,82],"structure-dependent":[72],"roles.":[73],"destroy":[76],"token":[77],"identity:":[78],"130M-parameter":[80],"language":[81],"using":[83],"only":[84,146],"reaches":[87],"C4":[88],"perplexity":[89],"260":[90],"(8x":[91],"GPT-2),":[92],"predictor":[95,100,128],"ablation":[96],"(replacing":[97],"linear":[99],"full":[102],"softmax":[103],"attention)":[104],"yields":[105],"identical":[106],"loss,":[107],"localizing":[108],"entire":[110],"gap":[111],"traces.":[114],"The":[115],"apply":[117],"lossy,":[118],"data-independent":[119],"compression;":[120],"by":[121],"data":[123],"processing":[124],"inequality,":[125],"downstream":[127],"recover":[130],"discarded":[132],"information.":[133],"Fixed-coefficient":[134],"accumulation,":[135],"whether":[136],"across":[137],"time":[138],"or":[139],"depth,":[140],"suffers":[141],"irreversible":[142],"information":[143],"dilution":[144],"that":[145],"learned,":[147],"input-dependent":[148],"selection":[149],"resolve.":[151]},"counts_by_year":[],"updated_date":"2026-04-14T06:08:25.285971","created_date":"2026-04-14T00:00:00"}
