{"id":"https://openalex.org/W7148398718","doi":"https://doi.org/10.48550/arxiv.2604.00208","title":"Measuring the Representational Alignment of Neural Systems in Superposition","display_name":"Measuring the Representational Alignment of Neural Systems in Superposition","publication_year":2026,"publication_date":"2026-03-31","ids":{"openalex":"https://openalex.org/W7148398718","doi":"https://doi.org/10.48550/arxiv.2604.00208"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00208","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00208","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.2604.00208","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132751647","display_name":"Sunny Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Liu, Sunny","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075774778","display_name":"Habon A. Issa","orcid":"https://orcid.org/0000-0001-7904-1582"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Issa, Habon","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120412203","display_name":"Andr\u00e9 Longon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Longon, Andr\u00e9","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132769007","display_name":"Liv Gorton","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gorton, Liv","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050983647","display_name":"Meenakshi Khosla","orcid":"https://orcid.org/0000-0002-2910-6242"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khosla, Meenakshi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5059395392","display_name":"David Klindt","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Klindt, David","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5132751647"],"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/T11094","display_name":"Face Recognition and Perception","score":0.3075000047683716,"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"}},"topics":[{"id":"https://openalex.org/T11094","display_name":"Face Recognition and Perception","score":0.3075000047683716,"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/T10581","display_name":"Neural dynamics and brain function","score":0.2531999945640564,"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/T10241","display_name":"Functional Brain Connectivity Studies","score":0.09269999712705612,"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/superposition-principle","display_name":"Superposition principle","score":0.6725999712944031},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.671999990940094},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6044999957084656},{"id":"https://openalex.org/keywords/projection","display_name":"Projection (relational algebra)","score":0.5145000219345093},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4959999918937683},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4839000105857849},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4681999981403351}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6769000291824341},{"id":"https://openalex.org/C27753989","wikidata":"https://www.wikidata.org/wiki/Q284885","display_name":"Superposition principle","level":2,"score":0.6725999712944031},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.671999990940094},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6371999979019165},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6044999957084656},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.5145000219345093},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4959999918937683},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4839000105857849},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4681999981403351},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41269999742507935},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.322299987077713},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31049999594688416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30480000376701355},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C2986949344","wikidata":"https://www.wikidata.org/wiki/Q9404","display_name":"Neural system","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.27239999175071716},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00208","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00208","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.2604.00208","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00208","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Comparing":[0],"the":[1,106,130,146,160,166,186,192],"internal":[2],"representations":[3,29],"of":[4,98,154],"neural":[5,23,35,178,194],"networks":[6,70],"is":[7,81],"a":[8,114,152],"central":[9],"goal":[10],"in":[11,39,180],"both":[12],"neuroscience":[13],"and":[14,66,184],"machine":[15],"learning.":[16],"Standard":[17],"alignment":[18,110],"metrics":[19,84],"operate":[20,38],"on":[21,87,105],"raw":[22,193],"activations,":[24],"implicitly":[25],"assuming":[26],"that":[27,56,82,159,176],"similar":[28,31],"produce":[30],"activity":[32],"patterns.":[33],"However,":[34],"systems":[36,134,142,179],"frequently":[37],"superposition,":[40],"encoding":[41],"more":[42,139],"features":[43,108,137,162,188],"than":[44,141,190],"they":[45,170],"have":[46],"neurons":[47],"via":[48],"linear":[49,67],"compression.":[50],"We":[51,173],"derive":[52],"closed-form":[53],"expressions":[54],"showing":[55],"superposition":[57,93,181],"systematically":[58],"deflates":[59],"Representational":[60],"Similarity":[61],"Analysis,":[62],"Centered":[63],"Kernel":[64],"Alignment,":[65],"regression,":[68],"causing":[69],"with":[71,117],"identical":[72],"feature":[73,124],"content":[74],"to":[75],"appear":[76,138],"dissimilar.":[77],"The":[78],"root":[79],"cause":[80],"these":[83],"are":[85,171],"dependent":[86],"cross-similarity":[88],"between":[89],"two":[90],"systems'":[91],"respective":[92],"matrices,":[94],"which":[95],"under":[96],"assumption":[97],"random":[99],"projection":[100],"usually":[101],"differ":[102],"significantly,":[103],"not":[104,150],"latent":[107],"themselves:":[109],"scores":[111],"conflate":[112],"what":[113],"system":[115],"represents":[116,120],"how":[118],"it":[119],"it.":[121],"Under":[122],"partial":[123],"overlap,":[125],"this":[126],"confound":[127],"can":[128],"invert":[129],"expected":[131],"ordering,":[132],"making":[133],"sharing":[135,143],"fewer":[136],"aligned":[140],"more.":[144],"Crucially,":[145],"apparent":[147],"misalignment":[148],"need":[149],"reflect":[151],"loss":[153],"information;":[155],"compressed":[156],"sensing":[157],"guarantees":[158],"original":[161],"remain":[163],"recoverable":[164],"from":[165],"lower-dimensional":[167],"activity,":[168],"provided":[169],"sparse.":[172],"therefore":[174],"argue":[175],"comparing":[177,191],"requires":[182],"extracting":[183],"aligning":[185],"underlying":[187],"rather":[189],"mixtures.":[195]},"counts_by_year":[],"updated_date":"2026-04-03T16:44:17.987007","created_date":"2026-04-03T00:00:00"}