Codette: Multi-Perspective Reasoning as aConvergentDynamical System with Meta-Cognitive Strategy Evolution

Codette: Multi-Perspective Reasoning as aConvergentDynamical System with Meta-Cognitive Strategy Evolution | 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 Article Codette: Multi-Perspective Reasoning as aConvergentDynamical System with Meta-Cognitive Strategy Evolution jonathan harrison This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9362560/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract We present Codette, a modular cognitive architecture that models multi-perspectivereasoning as a constrained dynamical system converging toward stable cognitive attractors.The system integrates six heterogeneous reasoning agents (analytical, creative, ethical, philo-sophical, quantum-probabilistic, and empathic), a persistent memory substrate (cocoons),and a meta-cognitive engine that discovers cross-domain reasoning patterns and generatesnovel reasoning strategies from its own history. The RC+ξ (Recursive Convergence + Epis-temic Tension) formalism provides a dynamical-systems-inspired lens for describing cognitivestate evolution via agent-weighted updates with coherence and ethical constraint terms; wetreat the convergence discussion as conditional on explicit modeling assumptions ratherthan as a general guarantee. We evaluate Codette through a benchmark suite of 17 prob-lems across six categories (multi-step reasoning, ethical dilemmas, creative synthesis, meta-cognition, adversarial robustness, and Turing naturalness) under four experimental condi-tions: single-agent baseline, multi-perspective synthesis, memory-augmented reasoning, andfull Codette with strategy evolution. On this benchmark (timestamp: 2026-04-08), the fullsystem achieves a 93.5% higher mean composite score than the single-agent baseline (0.356→ 0.689). Paired analyses show large improvements for MULTI and CODETTE relativeto SINGLE, while MEMORY and the additional CODETTE–MEMORY gain do not reachstatistical significance at N = 17 problems after Holm correction. The architecture runs onconsumer hardware (Llama 3.1 8B with LoRA adapters) and is open-source. Physical sciences/Mathematics and computing Biological sciences/Psychology Social science/Psychology Cognitive Architecture Multi-Agent Reasoning Epistemic Tension Dynamical Systems Meta-Cognition Ethical AI Strategy Evolution LoRA Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 May, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor invited by journal 16 Apr, 2026 Editor assigned by journal 10 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 08 Apr, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9362560","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":620427492,"identity":"5b1ac87e-b6e0-47a2-9102-bae0506de5f8","order_by":0,"name":"jonathan harrison","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBACAxCRAMT87A2Mj39UHACLHnhAjBbJngPMxgxnDjDwgLQkENICZtxIYJNmbINoYcCnxZz97LMPD/7Y5Ek2ALUUzrsjZy92+CHQFjs53QbsWix70o1nJLalFfMzHGC2nrntmTGPdJoBUEuysdkBHA47kMbMkNhwOHFmYwPjDd5thxN7pBNAWg4kbsOl5fwzZoaEP4cTNxxmYJDgnQPSkv4Bv5YbQFsS2IBajjEwSfM2gLTkELDlBtAWoF8SZ/YwMBvOOHbYmOd2TsGBBAM8fjmfxsz4449NYr/8A8YHH2oOy7HPTt/84UOFnRwuLUiA/wOyUQSVj4JRMApGwSjAAwDj2WX12haRZQAAAABJRU5ErkJggg==","orcid":"","institution":"Raiffs Bits LLC","correspondingAuthor":true,"prefix":"","firstName":"jonathan","middleName":"","lastName":"harrison","suffix":""}],"badges":[],"createdAt":"2026-04-09 03:23:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9362560/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9362560/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106725640,"identity":"8a06e092-7112-45b0-9f7a-a67fac7a3917","added_by":"auto","created_at":"2026-04-12 18:33:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":367294,"visible":true,"origin":"","legend":"","description":"","filename":"Codette.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9362560/v1_covered_ebf6994f-cee3-4e99-9665-10a630f91737.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCodette: Multi-Perspective Reasoning as aConvergentDynamical System with Meta-Cognitive Strategy Evolution\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cognitive Architecture, Multi-Agent Reasoning, Epistemic Tension, Dynamical Systems, Meta-Cognition, Ethical AI, Strategy Evolution, LoRA","lastPublishedDoi":"10.21203/rs.3.rs-9362560/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9362560/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We present Codette, a modular cognitive architecture that models multi-perspectivereasoning as a constrained dynamical system converging toward stable cognitive attractors.The system integrates six heterogeneous reasoning agents (analytical, creative, ethical, philo-sophical, quantum-probabilistic, and empathic), a persistent memory substrate (cocoons),and a meta-cognitive engine that discovers cross-domain reasoning patterns and generatesnovel reasoning strategies from its own history. The RC+ξ (Recursive Convergence + Epis-temic Tension) formalism provides a dynamical-systems-inspired lens for describing cognitivestate evolution via agent-weighted updates with coherence and ethical constraint terms; wetreat the convergence discussion as conditional on explicit modeling assumptions ratherthan as a general guarantee. We evaluate Codette through a benchmark suite of 17 prob-lems across six categories (multi-step reasoning, ethical dilemmas, creative synthesis, meta-cognition, adversarial robustness, and Turing naturalness) under four experimental condi-tions: single-agent baseline, multi-perspective synthesis, memory-augmented reasoning, andfull Codette with strategy evolution. On this benchmark (timestamp: 2026-04-08), the fullsystem achieves a 93.5% higher mean composite score than the single-agent baseline (0.356→ 0.689). Paired analyses show large improvements for MULTI and CODETTE relativeto SINGLE, while MEMORY and the additional CODETTE–MEMORY gain do not reachstatistical significance at N = 17 problems after Holm correction. 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