{"id":"https://openalex.org/W7119085439","doi":"https://doi.org/10.48550/arxiv.2601.01237","title":"Benchmarking the Computational and Representational Efficiency of State Space Models against Transformers on Long-Context Dyadic Sessions","display_name":"Benchmarking the Computational and Representational Efficiency of State Space Models against Transformers on Long-Context Dyadic Sessions","publication_year":2026,"publication_date":"2026-01-03","ids":{"openalex":"https://openalex.org/W7119085439","doi":"https://doi.org/10.48550/arxiv.2601.01237"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.01237","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.01237","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.2601.01237","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5122263257","display_name":"Abidemi Koledoye","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Koledoye, Abidemi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122242375","display_name":"Chinemerem Unachukwu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Unachukwu, Chinemerem","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122044082","display_name":"Gold Nwobu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nwobu, Gold","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122201151","display_name":"Hasin Rana","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rana, Hasin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5122263257"],"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/T13702","display_name":"Machine Learning in Healthcare","score":0.2736999988555908,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.2736999988555908,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.15940000116825104,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.08829999715089798,"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/benchmarking","display_name":"Benchmarking","score":0.8784000277519226},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.625},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6078000068664551},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.42820000648498535},{"id":"https://openalex.org/keywords/state-space","display_name":"State space","score":0.41029998660087585},{"id":"https://openalex.org/keywords/quadratic-equation","display_name":"Quadratic equation","score":0.3871000111103058}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.8784000277519226},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6215000152587891},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6078000068664551},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.42820000648498535},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41839998960494995},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.41029998660087585},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.3871000111103058},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37310001254081726},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3221000134944916},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C52918065","wikidata":"https://www.wikidata.org/wiki/Q230945","display_name":"State-space representation","level":2,"score":0.27059999108314514},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.26759999990463257},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.01237","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.01237","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.2601.01237","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.01237","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"State":[0],"Space":[1],"Models":[2],"(SSMs)":[3],"have":[4],"emerged":[5],"as":[6,50],"a":[7,31,51],"promising":[8],"alternative":[9],"to":[10,22,75],"Transformers":[11],"for":[12,96],"long-context":[13,44,100],"sequence":[14],"modeling,":[15],"offering":[16],"linear":[17],"$O(N)$":[18],"computational":[19,63],"complexity":[20],"compared":[21],"the":[23,36,40],"Transformer's":[24],"quadratic":[25],"$O(N^2)$":[26],"scaling.":[27],"This":[28],"paper":[29],"presents":[30],"comprehensive":[32],"benchmarking":[33],"study":[34],"comparing":[35],"Mamba":[37],"SSM":[38],"against":[39],"LLaMA":[41],"Transformer":[42],"on":[43],"sequences,":[45],"using":[46],"dyadic":[47],"therapy":[48],"sessions":[49],"representative":[52],"test":[53],"case.":[54],"We":[55],"evaluate":[56],"both":[57],"architectures":[58],"across":[59],"two":[60],"dimensions:":[61],"(1)":[62],"efficiency,":[64,81],"where":[65,82],"we":[66,83],"measure":[67],"memory":[68],"usage":[69],"and":[70,78,88],"inference":[71],"speed":[72],"from":[73],"512":[74],"8,192":[76],"tokens,":[77],"(2)":[79],"representational":[80],"analyze":[84],"hidden":[85],"state":[86],"dynamics":[87],"attention":[89],"patterns.":[90],"Our":[91],"findings":[92],"provide":[93],"actionable":[94],"insights":[95],"practitioners":[97],"working":[98],"with":[99],"applications,":[101],"establishing":[102],"precise":[103],"conditions":[104],"under":[105],"which":[106],"SSMs":[107],"offer":[108],"advantages":[109],"over":[110],"Transformers.":[111]},"counts_by_year":[],"updated_date":"2026-01-08T20:10:11.968330","created_date":"2026-01-08T00:00:00"}
