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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
- Human–AI Feedback Loop (Language–Cognition Loop)
- Fidelity Decay and Semantic Drift
- Proxy Optimization and Goodhart’s Law
- Language Compression and Cognitive Drift
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
- Articles & Essays (Substack): Semantic Fidelity Lab Substack
- Primary DOI Record: Figshare DOI Entry
- Concept Glossary: Semantic Fidelity Glossary
License
CC BY 4.0
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