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Introduces a new AI-native strategy to enhance visibility through clarity and identity resolution, suggesting improvements for AI systems.
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.
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Bernard Lynch (2026) studied this question.
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