| _version_ | 1866902152010530816 |
|---|---|
| author | Mas, Joseph |
| author_facet | Mas, Joseph |
| contents | <p><strong>Description:</strong></p> <p><strong>AI Visibility Empirical Finding: Supplementary Findings, Agentic Retrieval Behavior</strong></p> <p>This document records supplementary findings on agentic retrieval behavior observed concurrently with LLM training ingestion events in late January through early February 2026.</p> <p><strong>Key Finding</strong></p> <p>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.</p> <p><strong>What This Document Records</strong></p> <p>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.</p> <p><strong>Parent Study</strong></p> <p>Empirical Validation of AI Visibility Framework: Observed Multi-Platform Training Ingestion DOI: <a href="https://doi.org/10.5281/zenodo.18631595">https://doi.org/10.5281/zenodo.18631595</a></p> <p><strong>Canonical Reference</strong></p> <p>AI Visibility Theorem Set: <a href="https://josephmas.com/ai-visibility-theorems/">https://josephmas.com/ai-visibility-theorems/</a></p> <p><strong>Keywords:</strong></p> <p>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</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18781728 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI Visibility Empirical Finding: Supplementary Findings, Agentic Retrieval Behavior Mas, Joseph AI Visibility AI Visibility framework agentic retrieval behavior entity disambiguation LLM training ingestion upstream corpus development AI Visibility dual-layer optimization Common Crawl retrieval augmented generation empirical validation multi-platform ingestion <p><strong>Description:</strong></p> <p><strong>AI Visibility Empirical Finding: Supplementary Findings, Agentic Retrieval Behavior</strong></p> <p>This document records supplementary findings on agentic retrieval behavior observed concurrently with LLM training ingestion events in late January through early February 2026.</p> <p><strong>Key Finding</strong></p> <p>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.</p> <p><strong>What This Document Records</strong></p> <p>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.</p> <p><strong>Parent Study</strong></p> <p>Empirical Validation of AI Visibility Framework: Observed Multi-Platform Training Ingestion DOI: <a href="https://doi.org/10.5281/zenodo.18631595">https://doi.org/10.5281/zenodo.18631595</a></p> <p><strong>Canonical Reference</strong></p> <p>AI Visibility Theorem Set: <a href="https://josephmas.com/ai-visibility-theorems/">https://josephmas.com/ai-visibility-theorems/</a></p> <p><strong>Keywords:</strong></p> <p>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</p> |
| title | AI Visibility Empirical Finding: Supplementary Findings, Agentic Retrieval Behavior |
| topic | AI Visibility AI Visibility framework agentic retrieval behavior entity disambiguation LLM training ingestion upstream corpus development AI Visibility dual-layer optimization Common Crawl retrieval augmented generation empirical validation multi-platform ingestion |
| url | https://doi.org/10.5281/zenodo.18781728 |