Memory as Resonance: A Biomimetic Architecture for Infinite Context Memory on Ergodic Phonetic Manifolds

Memory as Resonance: A Biomimetic Architecture for Infinite Context Memory on Ergodic Phonetic Manifolds | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Memory as Resonance: A Biomimetic Architecture for Infinite Context Memory on Ergodic Phonetic Manifolds Tarik Houichime, Abdelghani Souhar, Younes EL Amrani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8428646/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The memory of contemporary Large Language Models is bound by a physical paradox: as they learn, they fill up. The linear accumulation (O(N)) of KeyValue states treats context as a warehouse of static artifacts, eventually forcing a destructive choice between amnesia and latency. We challenge this discrete orthodoxy, proposing that long-term memory is not the storage of items, but the persistence of a trajectory. We introduce Phonetic Trajectory Memory (PTM), a neuro-symbolic architecture that encodes language not as a sequence of tensors, but as a continuous path on an ergodic manifold governed by irrational rotation matrices. By decoupling the navigation (an invariant O(1) geometric signal) from the reconstruction (a probabilistic generative act), PTM achieves a compression magnitude of > 3, 000× relative to dense caches. We demonstrate that retrieval becomes a process of resonance: the phonetic trace stabilizes the model against hallucination via a “Signal Consensus” mechanism, securing up to ≈92% factual accuracy. While this aggressive abstraction alters generative texture, it unlocks immediate access latency(≈34ms) independent of depth. Our results suggest that infinite context does not require infinite silicon; it requires treating memory not as data to be stored, but as a reconstructive process acting on a conserved, undying physical signal. Artificial Intelligence and Machine Learning Large Language Models Long Context Memory Neuro-Symbolic Architecture Unitary Manifolds KV Cache Compression Phonetic Embeddings Reconstructive Memory Dynamical Systems. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8428646","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":564366474,"identity":"03fa8b4e-0242-4c00-8743-26d11896a8c3","order_by":0,"name":"Tarik Houichime","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie3OsUrDQBzH8V8IpEts1hQkvsJJQJeiD+JyoZAsPZ+gwz1AwTUu+grnkjnHH+pU3ESIQ7JkcnHTQe1ZB7Fwzepw3/H4f/gd4HL907REDfgIwIEEiNq95+EuSX/eBoi3JTDElMkhcj5ad7rEc8Lu/b7t5tNCPa5rvC1wEUnLSlgwrdCnjIJTllW5UM0l95YriLK2fSyHbkGZojCIs4oM4QwHEkLaSNT/IV8FM8T7MOTWRmKzon5JzVkzZ/73irKRJ7NSgtIJBSeGzI6vm5zT4SoWdxYyusr91yUoGT9QP3mvzo7GzUx3L4upuLGQbd7nzoM5jvfcu1wul2uoDW2aYwrTQHx4AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3477-4912","institution":"Mohammed V University In Rabat","correspondingAuthor":true,"prefix":"","firstName":"Tarik","middleName":"","lastName":"Houichime","suffix":""},{"id":564366475,"identity":"0ee0b246-a9a4-4f32-b54d-f939d65f0278","order_by":1,"name":"Abdelghani Souhar","email":"","orcid":"","institution":"Ibn Tofail University","correspondingAuthor":false,"prefix":"","firstName":"Abdelghani","middleName":"","lastName":"Souhar","suffix":""},{"id":564366476,"identity":"309e81b8-d74b-45ae-bca4-cc322a5151aa","order_by":2,"name":"Younes EL Amrani","email":"","orcid":"","institution":"Mohammed V University In Rabat","correspondingAuthor":false,"prefix":"","firstName":"Younes","middleName":"EL","lastName":"Amrani","suffix":""}],"badges":[],"createdAt":"2025-12-22 22:57:09","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8428646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8428646/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":99787921,"identity":"c510f593-ef00-458b-a2f7-8ca022bee281","added_by":"auto","created_at":"2026-01-08 12:41:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1454996,"visible":true,"origin":"","legend":"","description":"","filename":"PTM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8428646/v1_covered_67f60f89-2ea9-4ec8-af36-245579569c5d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMemory as Resonance: A Biomimetic Architecture for Infinite Context Memory on Ergodic Phonetic Manifolds\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"5EME AXE LLC","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Large Language Models, \tLong Context Memory, \tNeuro-Symbolic Architecture, \tUnitary Manifolds, \tKV Cache Compression, \tPhonetic Embeddings, \tReconstructive Memory, \tDynamical Systems.","lastPublishedDoi":"10.21203/rs.3.rs-8428646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8428646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe memory of contemporary Large Language Models is bound by a physical paradox: as they learn, they fill up. The linear accumulation (O(N)) of KeyValue states treats context as a warehouse of static artifacts, eventually forcing a destructive choice between amnesia and latency. We challenge this discrete orthodoxy, proposing that long-term memory is not the storage of items, but the persistence of a trajectory. We introduce Phonetic Trajectory Memory (PTM), a neuro-symbolic architecture that encodes language not as a sequence of tensors, but as a continuous path on an ergodic manifold governed by irrational rotation matrices. By decoupling the navigation (an invariant O(1) geometric signal) from the reconstruction (a probabilistic generative act), PTM achieves a compression magnitude of \u0026gt; 3, 000× relative to dense caches. We demonstrate that retrieval becomes a process of resonance: the phonetic trace stabilizes the model against hallucination via a “Signal Consensus” mechanism, securing up to ≈92% factual accuracy. 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