ENTRO-DASA — Dynamic Autonomous Sovereignty Algorithm

ENTRO-DASA (Dynamic Autonomous Sovereignty Algorithm) is a cybernetic governance framework designed for dissipative cognition systems operating under stochastic environmental perturbation. The framework introduces a mathematically governed architecture for maintaining semantic coherence, convergence stability, and entropy suppression within autonomous AI systems.The research combines concepts from dynamical systems theory, cybernetics, stochastic control, information geometry, computational epistemology, and artificial intelligence alignment. Rather than treating coherence as an emergent byproduct of large-scale optimization, ENTRO-DASA models cognitive stability as a dynamically enforceable invariant maintained through adaptive attractor governance.The framework introduces several original architectural mechanisms, including:Adaptive Linguistic Gravity (ALGR)Sovereign attractor enforcementMulti-trajectory synchronizationConsistency Basin confinementTemporal memory stabilizationEntropy suppression dynamicsStochastic Lyapunov convergence certificationThe system operates through synchronized trajectory swarms evolving inside a bounded cognitive state space under stochastic perturbation. Governance modules continuously regulate trajectory divergence, semantic instability, and entropy escalation through adaptive cybernetic feedback mechanisms.The study evaluates the framework using deterministic computational simulations across multiple stochastic noise regimes and governance configurations. Performance is measured using convergence and entropy metrics including:Convergence Concordance Score (CCS)Cognitive Entropy Reduction Index (CERI)False Divergence Rate (FDR)Steady-state stochastic deviation boundsPhase-transition critical thresholdsExperimental results demonstrate that the full ENTRO-DASA governance pipeline significantly improves attractor convergence stability, suppresses entropy escalation, and maintains trajectory coherence under moderate and high stochastic noise conditions compared to ungoverned baseline systems.The project is exploratory and foundational in nature, proposing a new direction for computational governance architectures capable of stabilizing autonomous reasoning systems operating in uncertain or adversarial informational environments.