Vacuum Intelligence: A Self-Organizing Reservoir with Hyperbolic Geometry and Anti-Fragile Meta-Learning

This working paper introduces the Vacuum Intelligence Framework, a neural-reservoir architecture in which adaptive computation emerges from a high-dimensional nonlinear stochastic system poised near a symmetry-breaking instability. Unlike conventional AI systems trained offline and deployed statically, this framework exploits inherent noise and criticality to remain sensitive and adaptable under novel disruptions. The reservoir state evolves according to a Langevin equation in a double-well potential, producing a vacuum at the origin that is inherently unstable yet generative. Perturbations break this symmetry, driving the system into one of 2N attractor basins whose selection depends on the history of inputs and noise. Synchronization metrics derived from hyperbolic embeddings in the Poincaré ball provide a continuous coherence signal, the synchronicity order parameter, that serves as an early-warning indicator of regime changes or supply chain disruptions. The core theoretical contribution is a meta-learning loop that interprets the decay-rebirth cycle as a simulated-annealing process over the reservoir’s structural parameters. This confers antifragility in the technical sense: the system improves in resilience after each controlled collapse because the Metropolis-sampled annealing process discovers parameter configurations that gradient-based methods cannot reach from within a local optimum. The paper presents the full mathematical formulation including the coupled stochastic differential equations, the hyperbolic distance metric, the synchronicity order parameter, the mean-time-to-recovery fitness function, and the Metropolis acceptance criterion. Application domains include defense logistics monitoring in contested environments, multi-domain command and control under degraded communications, and supply chain resilience forecasting under black-swan disruptions. A companion architecture specification document provides hardware mapping to memristor crossbar and neuromorphic substrates, and a TRL 2 to TRL 6 development roadmap.