What question did this study set out to answer?

The research aims to address reliability failures in large language models due to context drift and enhance safety by introducing a new memory mechanism.

February 11, 2026Open Access

Mitigating Large Language Model Context Drift via Holographic Invariant Storage

Key Points

The research aims to address reliability failures in large language models due to context drift and enhance safety by introducing a new memory mechanism.
Introduced Holographic Invariant Storage (HIS) as a neuro-symbolic memory mechanism.
Utilized high-dimensional hypervectors encoded to maintain safety constraints.
Conducted Monte Carlo simulation with 1,000 trials to assess the effectiveness of HIS.
Achieved a mean fidelity of 0.7074 with a standard deviation of 0.0039 under adversarial conditions.
Demonstrated that the mechanism maintains safety objectives in the presence of context noise.
Aligned findings with theoretical geometric bounds, validating the safety enforcement strategy.

Abstract

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 0.7074 (σ = 0.0039) even under direct adversarial attack. This result aligns with the theoretical geometric bound of 1/√2, proving that safety can be enforced as a deterministic structural constant.

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