Measuring Semantic Fidelity: A Practical Framework for Drift Evaluation in LLMs

This working paper is the third entry in the Semantic Drift series, part of the broader Reality Drift framework. It proposes a practical framework for measuring semantic fidelity in large language models. Building on earlier notes that defined semantic drift and outlined its cultural risks, this paper introduces operational heuristics—including baseline anchoring, recursive testing, and a 3-Step Drift Check—as first steps toward a benchmark for fidelity. Together with the prior papers, it positions semantic fidelity as a third evaluation axis alongside accuracy and coherence, highlighting the cultural and cognitive stakes of meaning preservation in AI systems.