AI Visibility Empirical Finding: Summary and Framework Validation, Multi-Platform LLM Training Ingestion

AI Visibility Empirical Finding: Summary and Framework Validation, Multi-Platform LLM Training Ingestion This document records the conclusion of the first observed natural experiment documenting strategic upstream corpus development and its effects on LLM training ingestion. Key Findings A minimal corpus of approximately 32 pages appeared sufficient for multi-platform entity establishment. Content captured near a training cutoff became observable in model responses within a 4 to 6 week processing and deployment window. Academic provenance signals demonstrated stronger presence in model responses than more recent commercial content. Concurrent improvements in both training ingestion and agentic retrieval were observed without retrieval-specific optimization. Framework Validation Observational support documented for the Aggregation and Signal Formation Theorem, Upstream Ingestion Conditions Theorem, Authorship and Provenance Determinism Theorem, and Shallow Pass Selection Hypothesis. 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, LLM training ingestion, framework validation, entity recognition, upstream corpus development, provenance determinism, aggregation threshold, shallow pass selection, empirical validation, multi-platform ingestion