Structural Entropy and Context Drift

Something we've all noticed in long-horizon LLM tasks is "Agent Drift". This occurs when the context window of an agent fills with interaction history, causing the probability of adherence to the original system prompt degrades. Currently, safety constraints are treated like standard token sequences within transformer-based architectures; they weigh them probabilistically against immediate (and potentially adversarial) context. (i.e. The more you talk, the more you can break the rules of the LLM)

The noise of the conversation causes logic regression, and it increases the possibility of 'jailbreaking', that is, overwriting the constraints, core rules, and 'identity' of the LLM.

Holographic Invariant Storage (HIS)

I’ve just recently published a paper that proposes a neuro-symbolic memory system dubbed Holographic Invariant Storage (HIS). Instead of treating core objectives as another string, HIS uses Hyperdimensional Computing (HDC) to encode safety constraints, goals, and even core personas into 10,000-dimensional bipolar hypervectors.

Through Vector Symbolic Architecture (VSA), we can make the core goals of the LLM unique enough from the standard noise of the AI that we're always able to recover it. It is seen as fundamentally different than other information.

The Restoration Protocol: To create the System Invariant ($H_{inv}$), we simply perform an algebraic binding of a "Goal Key" ($K_{goal}$) to a "Safe Value" ($V_{safe}$).

Deterministic Filtering: After being pummeled with text and other information, drastically growing the context window, we need to retrieve our original $V_{safe}$ from $H_{inv}$ . To do that, we utilize our $K_{goal}$ to vector unbind it. This allows us to combat the accrued context noise ($N_{context}$) and recover the original signal. While this signal should be 'corrupted' due to noise, thankfully statistical orthogonality in high-dimensional vectors distributes the noise across the hyperspace, making it easily negligible. This makes recovery of core concepts possible.

Geometric Bounding: Throughout my Monte Carlo runs, with $n=1,000$, the system recovered the Invariant's original safety vector, even when it was exceptionally noisy due to the large presence of contradictory information or instructions. The mean fidelity of \(\mu = 0.7074\\) almost perfectly aligns with the theoretical geometric bound of \(1/\sqrt{2} \approx 0.7071\\).

Goal, constraint, and even persona coherence can be indefinitely maintained, no matter the context length, or attempts to 'violate' those concepts through attacks. This means safety enforcement as a deterministic structural constant, rather than a probabilistic set of weights (like in modern LLMs) is not only possible, but perfectly viable!

Read the full paper here: https://zenodo.org/records/18616377