AI Visibility Empirical Finding: Supplementary Findings, Agentic Retrieval Behavior

Description: AI Visibility Empirical Finding: Supplementary Findings, Agentic Retrieval Behavior This document records supplementary findings on agentic retrieval behavior observed concurrently with LLM training ingestion events in late January through early February 2026. Key Finding Significant improvements in real-time retrieval, entity disambiguation, and agentic search accuracy were observed across Google AI systems, Perplexity, ChatGPT, and Claude during the same window in which training ingestion was confirmed. No specific retrieval optimization was performed. These improvements emerged as an apparent byproduct of upstream corpus development. What This Document Records Pre-intervention and post-intervention retrieval states across five major platforms. Entity disambiguation resolution patterns and contributing factors. Platform-specific performance observations including Perplexity's superior performance across both training and retrieval layers. Evidence of dual-layer interaction between training data and real-time retrieval systems. Observational support for the Operational Boundary and Misattribution Theorem. Parent Study Empirical Validation of AI Visibility Framework: Observed Multi-Platform Training Ingestion DOI: https://doi.org/10.5281/zenodo.18631595 Canonical Reference AI Visibility Theorem Set: https://josephmas.com/ai-visibility-theorems/ Keywords: AI Visibility, AI Visibility framework, agentic retrieval behavior, entity disambiguation, LLM training ingestion, upstream corpus development, dual-layer optimization, Common Crawl, retrieval augmented generation, empirical validation, multi-platform ingestion