Reality Drift Papers: AI, Representation, and Modern Systems

This collection explores the Reality Drift framework across artificial intelligence, representational systems, organizations, and modern digital society. The papers examine how systems gradually lose alignment with the realities they were created to represent while remaining internally coherent, optimized, and operational.Topics include semantic fidelity, AI alignment, grounding and evaluation, Goodhart's Law, proxy optimization, cybernetics, information theory, knowledge graphs, documentation drift, institutional drift, recursive compression, and organizational behavior. Together, these papers develop a common vocabulary for understanding how representations, metrics, procedures, and models can become progressively detached from the phenomena they describe.The collection is intended for researchers and practitioners working in artificial intelligence, machine learning, systems theory, organizational studies, information science, philosophy of technology, and human-computer interaction, as well as readers interested in modern information systems and representational failure.