AI Visibility Semantic Stability and Drift Theorem

This document formalizes the role of semantic stability in durable learning within large language models and explains how semantic drift degrades retention, recall, and attribution over time. It operates as a supporting theorem to the AI Visibility Canonical Definition and clarifies why stable meaning is required for long term learning independent of downstream optimization or interaction mechanisms.