AI Visibility Upstream Ingestion Conditions Theorem

This document formalizes the upstream ingestion conditions under which authored information becomes learnable by large language models. It operates as a supporting theorem to the canonical AI Visibility Canonical Definition and does not redefine the practice. The work distinguishes ingestion from interaction and defines how clarity semantic stability authorship and structural consistency influence whether information is retained as a durable internal representation. By separating learnability conditions from post ingestion interaction and optimization mechanisms this theorem establishes a boundary for AI Visibility and supports reproducible attribution and long term model recall across training cycles.