Signal Gap in AI-Mediated Retrieval: Namespace-Level Diagnostic Utility and Early-Stage Procedural Organization

Abstract

This study investigates the "Signal Gap" in contemporary Artificial Intelligence Information Retrieval (IR) and Retrieval-Augmented Generation (RAG) frameworks. Utilizing a dataset of 1,320 API-collected responses across four frontier models (Claude, Gemini, GPT, and Perplexity), the research evaluates how dynamic retrieval systems process initial web architecture signals using semantically opaque second-level domains to isolate namespace-level diagnostic utility.The complete, open-science artifact bundle including raw datasets, prompting codebooks, and Python replication scripts is publicly hosted at: https://github.com/regalred/diagnostic-utility