realitydriftproject/semantic-fidelity-framework

Semantic Fidelity Framework

A framework for evaluating whether AI systems preserve meaning, not just produce correct outputs.

Core Papers | Reality Drift Framework (2023–2026)

This repository contains the foundational papers of the Semantic Fidelity Lab, a research initiative focused on how meaning is preserved, degraded, and transformed in AI systems.

As generative models scale, most evaluation focuses on accuracy, coherence, and performance. This work introduces a different axis: semantic fidelity, the degree to which meaning remains intact across compression, transformation, and generation.

Across these papers, semantic fidelity is treated as a structural property of intelligent systems, not just a linguistic concern. The framework outlines how meaning degrades under optimization, recursion, and abstraction, even when outputs remain fluent and correct on the surface.

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Core Contributions

• Defines semantic fidelity as a missing dimension in AI alignment and evaluation
• Identifies semantic drift as a primary failure mode in generative systems
• Distinguishes accuracy from meaning preservation
• Introduces the compression paradox, where scaling increases fluency while degrading fidelity
• Explores how constraint collapse leads to loss of grounding
• Reframes hallucination as a downstream symptom rather than the root issue

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Contents

• SFL-01 — What Is Semantic Fidelity
  Introduces the core concept and establishes meaning preservation as a central problem in AI systems.

• SFL-02 — When Accuracy Isn’t Enough
  Explores the gap between surface correctness and preserved intent.

• SFL-03 — Measuring Fidelity Decay
  Examines how meaning degrades across generative processes.

• SFL-04 — The Compression Paradox in AI
  Analyzes how scaling and optimization affect semantic integrity.

• SFL-05 — Constraint Collapse and Fidelity Decay
  Describes how loss of grounding leads to drift.

• SFL-06 — Stop Calling It Hallucination
  Reframes common failure modes as semantic drift rather than randomness.

• SFL-07 — Language as Cognitive Exhaust
  Positions language as a compressed residue of thought.

• SFL-08 — A Semantic Fidelity Lexicon
  Defines key terms and concepts within the framework.

• SFL-09 — Autopoiesis and AI Alignment
  Explores self-referential systems and the role of internal constraints.

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Context

Part of the Semantic Fidelity Lab and the broader Reality Drift Framework (2023–2026), this work establishes semantic fidelity as a structural concern in AI alignment, evaluation, and system design.

Rather than optimizing for outputs alone, this framework focuses on whether systems remain meaningfully connected to the realities they are meant to represent.

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Core framework and sources

• Research Library (GitHub): Semantic Fidelity Lab Repository
• Articles & Essays (Substack): Semantic Fidelity Lab Substack
• Primary DOI Record: Figshare DOI Entry
• Concept Glossary: Semantic Fidelity Glossary