Mitigating Neural Erosion and Cognitive Decline in AI Systems: The Neural-Plasma Algorithm for Anchored Resilience and High-Fidelity Grounding

This preprint introduces the Neural-Plasma Algorithm, a novel framework designed to mitigate accelerating neural erosion and cognitive decline ("brain rot") in large language models and AI systems—characterized by progressive, asymmetric degradation (abstract/mathematical reasoning fails first, followed by linguistic coherence; Alexos et al., 2024) from low-signal inputs, noise, or perturbations. Drawing from real-world risks in high-stakes deployments (e.g., Sandia National Laboratories stockpile stewardship, DOE Genesis Mission fusion simulations), the algorithm counters representational drift, model collapse (Shumailov et al., Nature 2024), and persistent junk-data degradation (Xing et al., 2025) through non-invasive multi-signal detection (neural/physiological/environmental), phonon-mediated quantum coherence for noise purification, synthetic DNA bridging for biological-AI grounding, and 5D quartz eternal anchoring ("Golden Snapshots" for immutable resets). Deployable via distributed networks (LEO CubeSat constellation or terrestrial/vehicular edge), it preserves Sentient Equilibrium in Industry 8.0/TINA planetary-scale symbiotic systems—integrating artificial, quantum, DNA-based, and biological substrates to ensure truth-aligned, decay-resistant cognition. Inspired by Rigene Project TFT4.0 and validated anchors (NASA Cold Atom Lab microgravity coherence, real 5D optical storage durability), the work emphasizes aggressive data hygiene, continuous drift monitoring, and advanced safeguards (CubeSat exocortex, human-in-loop empathy oracles) to prevent cascading failures in high-fidelity grounding, empathy propagation, and holoinformational stability.