Mitigating Large Language Model Context Drift via Holographic Invariant Storage

As Large Language Models (LLMs) scale toward autonomous deployment, they face a critical reliability failure known as "Agent Drift." Over extended interaction sequences, the accumulation of context noise statistically dilutes the model's adherence to initial safety constraints and objective functions. This report introduces Holographic Invariant Storage (HIS), a neuro-symbolic memory mechanism based on Vector Symbolic Architectures (VSA). Unlike probabilistic attention mechanisms, HIS encodes safety constraints as high-dimensional hypervectors (D=10,000) that remain mathematically orthogonal to accumulated context noise. We demonstrate through Monte Carlo simulation (n=1,000) that this mechanism recovers original safety objectives with a mean fidelity of 0.7078 even under direct adversarial attack. This result aligns with the theoretical geometric bound of 1/sqrt(2), proving that safety can be enforced as a deterministic structural constant.