Update dataset README with semantic fidelity framework overview

Refines the dataset documentation to clearly define the scope and structure of the Semantic Fidelity frameworks.

Adds:

overview of meaning preservation vs accuracy in AI systems
structured breakdown of included frameworks
core concepts including semantic drift, fidelity decay, and proxy optimization
intended use and positioning as a conceptual resource

Clarifies the role of this dataset within the Semantic Fidelity layer of the Reality Drift framework.

README.md

@@ -1,3 +1,110 @@
----
-license: cc-by-4.0
----
+---
+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