This release contains two companion documents examining interpretation drift in large language models. The first paper establishes the empirical existence of interpretation drift, demonstrating that identical or near-identical inputs can yield meaningfully different interpretations across models, time, or context—even under deterministic decoding. Its focus is observational and measurement-oriented: determining whether interpretive variance occurs and how it manifests in practice. An artifact providing empirical grounding for interpretation drift framework is introduced in: Empirical Evidence Of Interpretation Drift In Arc-Style Rasoning [https://zenodo.org/records/18420425] The second document is a companion field guide that organizes those observations into a unified, descriptive taxonomy and a growing library of diagnosed cases. It does not attempt to explain or resolve drift; instead, it provides a structured vocabulary and diagnostic framework for recognizing recurring patterns of interpretive instability across domains such as code generation, go-to-market strategy, M&A analysis, and classification tasks. Together, these documents separate observation from organization. They are intended to support researchers and practitioners in reasoning clearly about interpretive variance in real-world systems, while explicitly reserving authority, judgment, and decision-making for human actors.