---
license: cc-by-4.0
task_categories:
- text-generation
- text-classification
- question-answering
language:
- en
tags:
- semantic-fidelity
- semantic-drift
- llm-evaluation
- ai-alignment
- meaning-loss
- fidelity-decay
- goodharts-law
- proxy-optimization
- co-cognition
- ai-cognition
- language-models
- llm-failure-modes
- ai-reliability
- human-ai-interaction
- cognitive-drift
pretty_name: 'Semantic Fidelity: AI Drift and Meaning Preservation Frameworks'
size_categories:
- n<1K
---
## Overview

Modern AI systems often appear to work.

Outputs are fluent, structured, and factually correct.

But correctness is not the same as understanding.

This dataset captures a recurring pattern:

> Systems remain accurate while meaning, intent, and grounding degrade.

Each document isolates a specific mechanism behind this failure and reframes common issues such as hallucination, evaluation gaps, and misalignment as expressions of a shared structural problem.

---

## Contents

- [Accuracy vs Semantic Fidelity](./accuracy-vs-semantic-fidelity-llm-evaluation-metrics.pdf)  
- [Human–AI Feedback Loop (Language–Cognition Loop)](./ai-feedback-loop-human-cognition-language-loop.pdf)  
- [Fidelity Decay and Semantic Drift](./ai-meaning-loss-fidelity-decay-llm-semantic-drift.pdf)  
- [Proxy Optimization and Goodhart’s Law](./ai-optimizing-proxies-goodharts-law-reality-drift.pdf)  
- [Language Compression and Cognitive Drift](./how-ai-changes-thinking-language-cognition-loop.pdf)  

---

## Concepts Covered

- Accuracy vs understanding (evaluation gap)  
- Semantic drift (gradual meaning misalignment)  
- Fidelity decay (loss of context and structure over time)  
- Proxy optimization (metrics vs reality)  
- Human–AI cognition loops (externalized thinking)  
- Language compression (loss of internal structure)  

---

## Core Concept: Semantic Fidelity

This dataset introduces **semantic fidelity** as a way to evaluate whether meaning and intent are preserved across:

- language  
- transformation  
- iteration  
- system boundaries  

Most evaluation approaches focus on correctness at the output level.  
This work focuses on whether meaning survives across the process.

---

## Intended Use

This dataset is useful for:

- analyzing LLM behavior beyond accuracy  
- understanding semantic drift and meaning loss  
- studying human–AI interaction and cognition  
- exploring alignment at the level of language and representation  
- building conceptual frameworks for AI system diagnostics  

---

## Not Intended For

This is not a benchmark dataset or training dataset.  
It is a conceptual and analytical resource for understanding AI system behavior.

---

## Core framework and sources

- Research Library (GitHub): [Semantic Fidelity Lab Repository](https://github.com/therealitydrift/semantic-fidelity-lab)  
- Articles & Essays (Substack): [Semantic Fidelity Lab Substack](https://semanticfidelitylab.substack.com/)  
- Primary DOI Record: [Figshare DOI Entry](https://doi.org/10.6084/m9.figshare.30422107)  
- Concept Glossary: [Semantic Fidelity Glossary](https://offbrandguy.com/semantic-fidelity-glossary/)  

---

## License

CC BY 4.0