Ambiguity Elimination as an AI-Native Visibility Strategy (v0.7)

This paper introduces ambiguity elimination as an AI-native visibility strategy: a structural approach that prioritises semantic clarity, identity resolution, and canonical anchoring over exposure-driven optimisation. Rather than attempting to increase visibility directly, the strategy removes competing origins of meaning so that AI systems must defer to a single canonical source. This paper complements the AI Visibility Lifecycle framework by explaining the optimisation logic that governs convergence, stability, and durability in AI-mediated discovery environments. Version 0.7 represents a pre-certification, pre-standardization release.