Semantic Fidelity Paper Series

These papers examine how meaning is preserved, distorted, and degraded as language is compressed, generated, retrieved, and transformed by artificial intelligence. They introduce semantic fidelity as a foundational concept for evaluating alignment, trust, and communicative integrity in generative systems, establishing a framework for understanding how compression, recursion, scale, and system design shape the preservation or erosion of meaning across human and machine intelligence.

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.
  

Documents — Part III: Drift, Constraint, and Alignment Failure

• Constraint Collapse and Fidelity Decay: When Feedback Stops Correcting Symbolic Systems SFL 05 (PDF)
  Explores how AI systems remain fluent while drifting from reality when feedback no longer enforces correction. Introduces constraint collapse and reframes alignment as a problem of preserving constraint, not just reducing error.

• Stop Calling It Hallucination: The True Failure Mode of AI Is Semantic Drift SFL 06 (PDF)
  Argues that hallucination is the wrong frame for AI failure. Replaces it with semantic drift, the gradual erosion of meaning across transformations, and establishes fidelity as the core evaluation lens.

• Language as Cognitive Exhaust: What Language Reveals About Thought, Compression, and AI SFL 07 (PDF)
  Reframes language as the compressed residue of thought. Connects human cognition and AI by showing how models learn from linguistic artifacts rather than raw experience.

• A Semantic Fidelity Lexicon: Preserving Meaning in the Age of Generative Systems SFL 08 (PDF)
  Defines the core vocabulary of semantic fidelity, including semantic drift, fidelity decay, and meaning collapse. Establishes the conceptual foundation for measuring and governing meaning in AI systems.

• Autopoiesis Is the Missing Variable in AI Alignment: Why Systems That Cannot Preserve Themselves Cannot Truly Align SFL 09 (PDF)
  Introduces autopoiesis as the missing constraint in AI alignment. Argues that systems without internal consequence remain only superficially aligned and cannot reliably preserve meaning.

Related Framework Concepts

Semantic Fidelity Resources:

• Semantic Fidelity Definition
• Glossary
• Visual Frameworks
• Semantic Fidelity Paper Series
• Failure Modes in LLM Systems
• Drift Detection in AI Systems
  

Core Concepts:

• Reality Drift
• Optimization Trap
• Constraint Collapse
• Recursive Compression