AI Systems and Reality Drift

A collection of framework notes exploring AI evaluation, alignment, grounding, semantic fidelity, retrieval systems, incentive structures, and representational failure modes through the Reality Drift framework. This component examines how AI systems can remain coherent, operational, and apparently successful while gradually losing fidelity to the realities they were designed to model. Topics include benchmarking, model monitoring, hallucination, distribution shift, semantic alignment, retrieval-augmented generation (RAG), principal-agent problems, KPI distortion, Campbell's Law, and the map-territory distinction. Included Papers: • AI Evaluation, Benchmarking, and Reality Drift • AI Reliability, Model Monitoring, and Reality Drift • Hallucination, Distribution Shift, Alignment Failure, and Reality Drift • Hallucination, Grounding, Faithfulness, and Reality Drift • The Map-Territory Distinction and Semantic Fidelity • Performance Metrics, KPI Distortion, and Campbell's Law • Principal-Agent Problems, Incentive Misalignment, and Reality Drift • Retrieval-Augmented Generation, Grounding, Faithfulness, and Reality Drift • Semantic Alignment, Meaning Preservation, and Reality Drift