Semantic Fidelity Project – Archive
The Semantic Fidelity Project examines what happens to meaning as information is translated, compressed, summarized, retrieved, generated, and reused across human and artificial systems.
The project focuses on whether context, intent, hierarchy, uncertainty, provenance, and relational meaning survive transformation. Its work develops concepts and methods for understanding Semantic Fidelity, Semantic Drift, Fidelity Decay, Recursive Compression, Constraint Collapse, Ground Erosion, and related forms of representational failure.
The project now functions as a specialized branch of the broader Reality Drift framework. Within the framework, Semantic Fidelity provides a way to examine what is preserved or lost as representations move across layers, while Reality Drift addresses the larger system-level condition in which those representations can remain operational even as corrective contact with underlying reality weakens.
Research Collection
Semantic Fidelity Project — Figshare
The research collection, including the Semantic Fidelity Project paper series, lexicon, evaluation frameworks, and related research materials.
Selected Essays
When Accuracy Isn’t Enough: Toward Semantic Fidelity in AI Systems
An introduction to Semantic Fidelity as a problem beyond factual accuracy.
The Compression Paradox: Why Meaning Breaks Before Models Fail
How compressed representations can remain usable while losing distinctions necessary for interpretation.
Constraint Collapse: When Feedback Stops Correcting Symbolic Systems
How feedback can remain present while losing the ability to meaningfully constrain or revise a system.
Measuring Fidelity Decay: Tracking Meaning Collapse in Generative Systems
An exploration of how semantic degradation might be identified and evaluated across transformations.
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GitHub Repository
View the Semantic Fidelity Project on GitHub →
The structured repository for the project, preserving papers, definitions, research materials, evaluation tools, and supporting documentation.