Measuring Semantic Fidelity: A Practical Framework for Drift Evaluation in LLMs (Early Working Note)

This early working note explores practical approaches for measuring semantic fidelity in large language models. Building on earlier notes that introduced semantic drift as a hidden failure mode, this paper develops initial operational heuristics for identifying and evaluating fidelity loss across recursive transformations, including baseline anchoring, recursive testing, and a 3-Step Drift Check.

The note treats semantic fidelity as a distinct evaluative dimension alongside factual accuracy and coherence, arguing that current benchmarks often fail to capture whether meaning, intent, and contextual purpose remain intact. It situates these early measurement proposals within a broader concern for representational degradation in AI systems and the cultural consequences of recursive semantic compression.

This document preserves an early developmental stage of the Semantic Drift series and reflects the initial trajectory of what later became the Semantic Fidelity framework. Subsequent work refined these concepts into a broader canonical model of fidelity preservation and drift detection.

Part of the Reality Drift framework by A. Jacobs.