Semantic Fidelity Lab Series: Preserving Meaning in the Age of Artificial Intelligence
A collection of core documents from the Semantic Fidelity Lab within the Reality Drift framework.
These papers examine how meaning is preserved, distorted, and degraded as language is compressed, generated, and transformed by artificial intelligence. They introduce semantic fidelity as a foundational concept for evaluating alignment, trust, and communicative integrity in generative systems.
Together, they establish a unified framework for understanding how compression, recursion, and scale shape the preservation—or erosion—of meaning across human and machine intelligence.
This series captures the emergence of semantic fidelity as a formal research domain at the intersection of AI alignment, language, and information theory.
Documents — Part I: Foundations of Semantic Fidelity
- What Is Semantic Fidelity? Preserving Meaning in the Age of Artificial Intelligence (SFL 01) (PDF)
Introduces semantic fidelity as the preservation of intent, nuance, and communicative purpose across transformations of language.
- When Accuracy Isn’t Enough: Semantic Fidelity in AI Systems (SFL 02) (PDF)
Explains why correctness alone is insufficient for AI alignment and establishes fidelity as the missing dimension in evaluating generative systems.
Documents — Part II: Measurement, Compression, and Alignment in AI
- Measuring Fidelity Decay in Generative Systems: How Meaning Erodes Under Compression, Recursion, and Scale (SFL 03) (PDF)
Presents a framework for quantifying semantic drift and fidelity decay, introducing metrics for evaluating meaning preservation in AI.
- The Compression Paradox in AI: Why Meaning Breaks Before Models Hallucinate (SFL 04) (PDF)
Examines how recursive compression degrades semantic integrity and establishes fidelity as a central concern in AI alignment.
Related Items: Semantic Fidelity Lab Canonical Glossary
This work originated as part of the Semantic Fidelity Lab (2024–2026) and is integrated into the broader Reality Drift framework. This site functions as a lightweight archive and reference layer. Primary essays and long-form writing are distributed across external platforms:
Substack · GitHub · DOI · Slideshare
Part of the Reality Drift Framework by A. Jacobs (2023–2026)