S-AI-GSF: Controlled Global Self-Formation under Symbolic, Hormonal, and Decidability-Preserving Constraints

S-AI-GSF: Controlled Global Self-Formation under Symbolic, Hormonal, and Decidability-Preserving Constraints | 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 S-AI-GSF: Controlled Global Self-Formation under Symbolic, Hormonal, and Decidability-Preserving Constraints Said Slaoui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9695425/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 Sparse Artificial Intelligence and Global Self-Formation are explored in this work through a framework of controlled structural evolution in which an intelligent system can adapt its own architecture while preserving formal decidability guarantees. The approach relies on formation parsimony, hormonal governance mechanisms, and two complementary regulatory hormones — Stabiline and Evolutine — that coordinate bounded structural adaptation across tasks without compromising logical correctness or system stability. The framework integrates Lyapunov stability, entropic contraction, and decidability-preserving validation into a unified theory of safe self-modification based on symbolic reasoning and recursive language theory. A modifiable/non-modifiable zone partition constrains architectural evolution so that only formally authorised regions of the system may evolve, ensuring controlled inter-task architectural transformation under explicit verification constraints. The resulting model establishes a quadruple cognitive equivalence linking convergence, symbolic coherence, stability, and decidability within a parsimonious multi-agent intelligence architecture designed for safe and explainable structural self-evolution. Artificial Intelligence and Machine Learning Sparse Artificial Intelligence Global Self-Formation Controlled Structural Evolution Decidability Preservation Formation Parsimony Hormonal Governance Stabiline Evolutine Lyapunov Stability Entropic Contraction Modifiable/Non-Modifiable Zone Partition Decidability-Preserving Validation Bounded Structural Adaptation Inter-Task Architectural Evolution Quadruple Cognitive Equivalence Symbolic Reasoning Multi-Agent Systems Recursive Language Theory Parsimonious Intelligence Safe Self-Modification. 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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