Lookup‑Verified Inference: A Lightweight Mechanism for Preventing Structural Drift in AI Systems

Introduction

Artificial intelligence systems increasingly operate in settings that require stable, reliable reasoning over long inference chains. However, modern transformer‑based models exhibit a well‑documented failure mode known as structural drift: the tendency to collapse uncertainty into inferred assumptions and subsequently treat those assumptions as factual. Once an unverified inference becomes part of the model’s internal context, it can propagate through subsequent reasoning steps, amplifying errors and causing divergence from reality.

Existing mitigation strategies—such as reinforcement learning from human feedback, guardrail systems, and retrieval‑augmented generation—attempt to correct drift at the output layer. These approaches can shape behaviour, but they do not address the underlying mechanism that produces drift within the inference process itself. As a result, long‑context reasoning, autonomous agents, and safety‑critical applications remain vulnerable to compounding hallucinations and unstable internal states.

Lookup‑Verified Inference (LVI) introduces a lightweight, architecture‑agnostic solution. Instead of preventing models from inferring unknowns, LVI allows free inference but marks uncertain values as provisional and verifies them before they are used as foundations for further reasoning. This verification step can be implemented using a simple cross‑reference mechanism, ensuring that provisional assumptions do not silently become structural facts.

LVI provides a minimal, practical path toward drift‑resistant inference without restricting creativity, autonomy, or generative capability. It is designed to function as a universal inference primitive that can be integrated into existing systems with minimal overhead.

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