| --- |
| 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. |
|
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| But correctness is not the same as understanding. |
|
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| This dataset captures a recurring pattern: |
|
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| > Systems remain accurate while meaning, intent, and grounding degrade. |
|
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| 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 |
