The Entropy of Recursion: A Strategic Framework for Provenance-Verified Data and the Preservation of Cognitive Integrity in Generative Systems

The rapid proliferation of generative artificial intelligence has precipitated a crisis of data integrity, characterized by Model Collapse—a degenerative process where recursive training on synthetic output eliminates statistical variance and induces systematic hallucination. This paper argues that the prevailing paradigm of indiscriminate Data Accumulation must yield to a new era of Data Stewardship. We introduce the Provenance-Embedded Semantic Transport (PEST) framework, a novel architecture that inextricably binds semantic context to media artifacts via robust perceptual hashing and decentralized storage. We validate this approach through the Alexandria Aeternum corpus: 10,097 museum-verified artworks enriched with 50+ metadata fields per image (DOI: 10.5281/zenodo.18359131). We conclude by outlining the economic implications of this framework, specifically the emergence of a Verification Economy where cryptographically verified human data becomes a scarce, priced asset class essential for stabilizing future foundation models against the mathematical inevitability of recursive degeneration.