Mitigating Large Language Model Context Drift via Holographic Invariant Storage

Scaling towards autonomous deployment has been a key goal of Large Language Mod-
els. However, “Agent Drift” has caused critical issues and reliability failure. Adherence to
constraints within LLM context windows over extended interaction sequences causes initial
instructions, objective functions, and safety constraints to significantly diverge from their
original state. In this report, we introduce Vector Symbolic Architectures (VSA) and a
neuro-symbolic memory system, dubbed Holographic Invariant Storage (HIS), which en-
codes the existing safety mechanisms and rules into high-dimensional vectors of dimension
D = 10, 000, called hypervectors. Due to the nature of high-dimensionality, the encoded
rules are now mathematically orthogonal to the noise of the context window. Modern prob-
abilistic attention mechanisms do not utilize systems like this because of fundamentally
different mathematical principles, with VSAs using algebraic operations on vectors and
probability-driven machines using softmax-based weighting. To prove that safety can in-
deed be enforced as a deterministic structural constant, we established a fidelity aligning to
the (theoretical) geometric bound: 1/√2. Through Monte Carlo simulations (of n = 1000),
even under direct adversarial attacks on those safety constraints, we have proven that we
can recover the original safety rules and core objectives of the system with a mean fidelity
of 0.7074 (σ = 0.0039), which aligns with the geometric bound.