Neural-Plasma Audit Protocol (NPAP): Operational Framework for Decay-Resistant AI in National Security and Fundamental Physics

This preprint presents the Neural-Plasma Audit Protocol (NPAP), an operational framework operationalizing the Neural-Plasma Algorithm for decay-resistant AI in high-stakes environments (Sandia nuclear stewardship, CERN HL-LHC anomaly detection). NPAP provides a standardized, auditable lifecycle—multi-layered entropy monitoring, Method 3-666 threshold intervention, coherence rehabilitation (phonon purification, 5D quartz resets), and empathy-oracle validation—to prevent neural erosion, representational drift, and "brain rot" (Alexos et al., 2024; Shumailov et al., 2024; Xing et al., 2025). It aligns with DOE Genesis Mission objectives (doubling productivity via resilient AI; Executive Order Nov 2025, 26 Challenges Feb 2026) and CERN CAISC principles (explainability, accountability, human-in-loop). Applications include immutable baselines for Sandia stockpile simulations and noise-gated triggers for CERN rare-event detection, preserving Sentient Equilibrium in Industry 8.0/TINA networks. Companion to core framework: https://zenodo.org/records/18718403 (restricted) and Tesla adaptation: https://zenodo.org/records/18724358 (restricted).