Asynchronous Causal Foreknowledge: A Neuromorphic Oracle for Pre-Symmetry Breaking Detection in Non-Stationary Environments

This research provides the first empirical validation of Asynchronous Causal Foreknowledge in a natively patched Intel Lava environment. We address the "Nyquist-Synchronous Bottleneck"—the fundamental inability of clock-driven AI (GPUs) to perceive high-frequency causal precursors in non-stationary physical systems. By implementing a Neuromorphic Active Inference Engine (NAIE) on the Loihi 2 architecture, we demonstrate the extraction of 500Hz "pre-cursor" oscillations with a +53.1ms Temporal Alpha over traditional digital baselines. The paper proves that a system governed by Variational Free Energy can maintain 100% metabolic stasis during noise while identifying symmetry-breaking events with sub-millisecond latency. This is a foundational proof for real-time causal control in Scientific ASI domains.