This paper addresses a critical vulnerability in frontier artificial intelligence systems: the tendency for safety guardrails to degrade or "drift" under complex, adversarial user prompts. To resolve this, we formalize the Value Drift Invariance Theorem (VDIT), a novel mathematical architecture that transitions AI alignment from a reactive practice into a verifiable system invariant. The framework models the real-time mathematical degradation of safety guardrails and baseline utility capacity under systemic optimization pressures and dynamic, user-driven value shifts. To counteract intentional semantic exploitation, the framework re-engineers the epistemic parsimony principles of Joshua's Razor into an active computational defense layer. Under this condition, user requests requiring high-density, ad-hoc logical justifications trigger an exponential product penalty that mathematically flattens the requested value shift to zero. By binding these variables to a strict master inequality, the architecture guarantees that a model's operational safety parameters can never degrade below their uncorrupted baseline states, offering a deterministic, verifiable approach to robust and invariant frontier model alignment.
Joshua O. Bautista (Fri,) studied this question.