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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 |