{"id":"https://openalex.org/W7128949136","doi":"https://doi.org/10.5281/zenodo.18650280","title":"Atomic Vector Symbolic Architecture: Physics-Inspired Hyperdimensional Computing for Explainable AI","display_name":"Atomic Vector Symbolic Architecture: Physics-Inspired Hyperdimensional Computing for Explainable AI","publication_year":2026,"publication_date":"2026-02-15","ids":{"openalex":"https://openalex.org/W7128949136","doi":"https://doi.org/10.5281/zenodo.18650280"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.18650280","doi":"https://doi.org/10.5281/zenodo.18650280","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18650280","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.18650280","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134144245","display_name":"Muhammad Arshad","orcid":"https://orcid.org/0009-0002-1314-0494","observed_orcids":["https://orcid.org/0009-0002-1314-0494"]},"institutions":[{"id":"https://openalex.org/I4405254509","display_name":"Independent Research Association","ror":"https://ror.org/01wsw8g60","country_code":"RO","type":"nonprofit","lineage":["https://openalex.org/I4405254509"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Arshad, Muhammad","raw_affiliation_strings":["Independent Research Association"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Independent Research Association","institution_ids":["https://openalex.org/I4405254509"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5134144245"],"corresponding_institution_ids":["https://openalex.org/I4405254509"],"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.4722000062465668,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.4722000062465668,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.3540000021457672,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.040800001472234726,"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/hyperdimensional-computing","display_name":"hyperdimensional computing","score":0.9660000205039978},{"id":"https://openalex.org/keywords/clinical-triage","display_name":"clinical triage","score":0.9139999747276306},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.8659999966621399},{"id":"https://openalex.org/keywords/icd-11","display_name":"ICD-11","score":0.8230000138282776},{"id":"https://openalex.org/keywords/comorbidity-detection","display_name":"comorbidity detection","score":0.8889999985694885},{"id":"https://openalex.org/keywords/holographic-reduced-representations","display_name":"holographic reduced representations","score":0.878000020980835},{"id":"https://openalex.org/keywords/deterministic-inference","display_name":"deterministic inference","score":0.8650000095367432},{"id":"https://openalex.org/keywords/audit-trail","display_name":"audit trail","score":0.640999972820282},{"id":"https://openalex.org/keywords/low-latency-inference","display_name":"low-latency inference","score":0.5789999961853027},{"id":"https://openalex.org/keywords/on-premises-deployment","display_name":"on-premise deployment","score":0.6330000162124634}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6304000020027161},{"id":"https://openalex.org/C2777120189","wikidata":"https://www.wikidata.org/wiki/Q780067","display_name":"Triage","level":2,"score":0.5906999707221985},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.48170000314712524},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4747999906539917},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.398499995470047},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3643999993801117},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34700000286102295},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3450999855995178},{"id":"https://openalex.org/C199521495","wikidata":"https://www.wikidata.org/wiki/Q181487","display_name":"Audit","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.3052000105381012},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2879999876022339},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.27799999713897705},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.2531000077724457},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.18650280","doi":"https://doi.org/10.5281/zenodo.18650280","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18650280","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},{"id":"doi:10.5281/zenodo.18650281","doi":"https://doi.org/10.5281/zenodo.18650281","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18650281","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"}],"best_oa_location":{"id":"doi:10.5281/zenodo.18650280","doi":"https://doi.org/10.5281/zenodo.18650280","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.18650280","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":[{"id":"https://metadata.un.org/sdg/3","score":0.7246000170707703,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7212124215","https://openalex.org/W7223440899","https://openalex.org/W7168895818","https://openalex.org/W7168145680","https://openalex.org/W7156311418","https://openalex.org/W7214052218","https://openalex.org/W7165516140","https://openalex.org/W7164669374","https://openalex.org/W7214770543","https://openalex.org/W7169090817"],"abstract_inverted_index":{"This":[0],"study":[1],"introduces":[2],"the":[3],"Atomic":[4,98],"Vector":[5],"Symbolic":[6],"Architecture":[7],"(Atomic":[8],"VSA),":[9],"a":[10,103],"deterministic":[11,112],"framework":[12],"grounded":[13],"in":[14],"Hyperdimensional":[15],"Computing":[16],"(HDC)":[17],"for":[18,40,96,109],"clinical":[19,32],"triage":[20,33],"and":[21,73,116],"explainable":[22],"AI.":[23],"Key":[24],"Results:\u2022":[25],"92.5%":[26],"F1":[27],"Score":[28],"on":[29,48],"25-category":[30],"ICD-11":[31],"(Winner-Take-All,":[34],"no":[35],"tuning)\u2022":[36],"91.9%":[37],"label":[38],"recall":[39],"multi-label":[41],"comorbidity":[42],"detection\u2022":[43],"11.97ms":[44],"median":[45],"inference":[46],"latency":[47],"commodity":[49],"CPU":[50],"(15W)\u2022":[51],"Algebraically-traceable":[52],"decisions":[53],"enabling":[54,68],"full":[55],"audit":[56],"trails":[57],"The":[58],"system":[59],"employs":[60],"10,048-dimensional":[61],"bipolar":[62],"vectors":[63],"with":[64,86],"holographic":[65],"reduced":[66],"representations,":[67],"semantic":[69,84],"composition":[70],"through":[71],"binding":[72],"bundling":[74],"operations.":[75],"We":[76],"document":[77],"an":[78],"8%":[79],"accuracy":[80],"ceiling":[81],"due":[82],"to":[83,106],"clones":[85],"100%":[87],"symptom":[88],"overlap\u2014a":[89],"fundamental":[90],"symptom-encoding":[91],"limit":[92],"requiring":[93],"lab":[94],"values":[95],"disambiguation.":[97],"VSA":[99],"is":[100],"positioned":[101],"as":[102],"complementary":[104],"paradigm":[105],"neural":[107],"networks":[108],"scenarios":[110],"prioritizing":[111],"inference,":[113],"on-premise":[114],"deployment,":[115],"regulatory":[117],"compliance.":[118]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-02-16T00:00:00"}
