Semantic Fidelity Lab Series: Drift Detection in AI Systems
A collection of core documents examining how modern AI systems degrade over time while appearing stable. These frameworks focus on a central problem in AI systems: outputs remain coherent, metrics remain stable, and systems continue to function, yet alignment with real-world conditions and user intent gradually weakens.
Across model monitoring, evaluation, and governance, drift is often treated as a narrow technical issue. These documents reframe drift as a multi-layer condition spanning data, behavior, meaning, and system-level feedback. As AI systems scale, optimize, and operate on compressed representations, failure becomes less visible. Systems do not break. They continue working while slowly disconnecting from reality.
This collection introduces drift detection as both a technical and structural problem, providing practical frameworks for identifying where alignment is degrading and why standard metrics fail to capture it. Together, these documents map how drift emerges across modern AI systems, from measurable performance changes to silent failures in meaning and intent.
Documents — Drift Detection in AI Systems
Detecting Silent Model Drift in LLM Systems (SFL 01) [PDF]
Explains how large language models degrade without triggering metric failures, producing outputs that remain fluent but lose alignment with intent, context, and usefulness. [DOI] [Github] [Hugging Face] [IA]
Drift Audit Checklist (AI Systems) (SFL 02) [PDF]
A practical checklist for identifying drift across data, performance, behavioral, semantic, and system layers in production AI systems. [DOI] [Github] [Hugging Face] [IA]
Model Drift Detection Framework (SFL 03) [PDF]
A structured framework for detecting and evaluating model drift across statistical, behavioral, and semantic layers, including methods for monitoring and mitigation. [DOI] [Github] [Hugging Face] [IA]
Institutional Drift Detection Framework (SFL 04) [PDF]
Extends drift detection beyond AI systems into organizations, showing how systems maintain performance while losing alignment with real-world outcomes. [DOI] [Github] [Hugging Face] [IA]
Additional Resources
- Semantic Fidelity Glossary [PDF] [DOI]
- Semantic Fidelity Concept Framework [PDF] [DOI]
- Failure Modes in LLM Systems
- Preserving Meaning in the Age of Artificial Intelligence
Note: This site functions as a lightweight archive and reference layer for the Reality Drift framework. Primary essays and long-form writing are distributed across external platforms:
Substack • GitHub • DOI • Slideshare
Part of Reality Drift Framework by A. Jacobs (2023-2026)