AI Visibility Semantic Stability and Drift Theorem
Abstract
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.
Fields of Study & Subjects
Language and cultural evolution
Machine Learning and Algorithms
Domain Adaptation and Few-Shot Learning
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 19 January 2026