Add AI drift detection frameworks and evaluation PDFs

Adds four core documents covering drift detection, evaluation, and system misalignment in AI systems and LLM workflows.

Includes:

LLM drift detection and silent model degradation
AI model audit checklist for production systems
Model drift evaluation framework across multiple layers
Institutional drift detection framework for complex systems

These documents provide a structured approach to identifying drift beyond standard metrics, including behavioral, semantic, and system-level misalignment.

Focus is on evaluating alignment, usefulness, and real-world performance over time, not just accuracy or benchmark results.

.gitattributes

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+ai-model-audit-checklist-drift-detection.pdf filter=lfs diff=lfs merge=lfs -text
+institutional-drift-detection-framework.pdf filter=lfs diff=lfs merge=lfs -text
+llm-drift-detection-why-ai-outputs-degrade-without-errors.pdf filter=lfs diff=lfs merge=lfs -text
+model-drift-detection-framework-machine-learning.pdf filter=lfs diff=lfs merge=lfs -text

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