MetaFlux AI: Attractor-Based Memory in Dynamical Systems

MetaFlux AI: Stable Memory via Mean-Field Feedback in Dynamical Systems Overview MetaFlux introduces a dynamical systems architecture for artificial intelligence in which memory emerges from stabilized global states, rather than being explicitly stored. Instead of relying on static parameters or discrete memory modules, MetaFlux encodes memory as a persistent attractor within a system of interacting units. Core Idea Classical synchronization systems produce transient coherence without persistence.MetaFlux extends these systems by introducing: Global mean-field aggregation (Z) Feedback-driven stabilization Attractor-based memory formation The global state is defined as: MetaFlux Mean-Field Equation Z = (1/N) Σₖ rₖ e^(iϕₖ) Where: (Z): Global mean-field state (collective system representation) (N): Number of oscillators (r_k): Amplitude of the k-th oscillator (\phi_k): Phase of the k-th oscillator This equation defines how local oscillator states aggregate into a single global state that enables stabilization and memory formation in MetaFlux systems. Feedback from this global state stabilizes the system, enabling long-lived memory states. Memory as a Dynamical Attractor Memory is not stored explicitly. Instead: Input perturbs the system Oscillators synchronize through interaction A global state ( Z ) emerges Feedback reinforces this configuration The system converges to a stable attractor Memory = stabilized system dynamics Architecture Pipeline: Input → Oscillators → Mean-Field (Z) → Feedback → Stable State → Readout Oscillators: local dynamic units Global state (Z): collective representation Feedback loop: enforces stability Readout: extracts output from attractor Key Advantages Persistent Memory — no explicit storage required Noise Robustness — attractors resist perturbations Energy Efficiency — no continuous rewriting Unified Compute + Memory — processing and storage are integrated Positioning in AI MetaFlux connects to: Dynamical systems AI Reservoir computing Recurrent / continuous-time neural systems Neuromorphic computing Key distinction:MetaFlux transforms transient synchronization into persistent memory via mean-field feedback. Resources Zenodo Record: https://zenodo.org/records/18384787 GitHub Repository: https://github.com/srikarr20/MetaFlux-AI-Physics-Memory-Substrate One-Line Summary Memory is not stored — it stabilizes