Ouroboration: Recursive Provenance Loss and False Corroboration in AI-Mediated Information Systems

This concept note proposes “Ouroboration” as a provisional term for a recursive information process in which AI-generated claims, expressions, biases, or errors are reintroduced into prompts, search results, citations, datasets, or other AI systems; their common provenance and dependency relations become obscured; and derivative materials are consequently treated as if they provided independent corroboration. The proposed construct is defined by four necessary conditions: AI-mediated origin, recursive re-entry, provenance opacity, and pseudo-independence. Increased belief, authority, persistence, normalization, or error amplification are treated as possible outcomes rather than defining conditions. The note distinguishes ouroboration from model collapse, model autophagy, circular reporting, citogenesis, and the illusion of consensus, and presents falsifiable propositions and a pilot experimental design. This is a dated, citable concept proposal, not a claim that the term is already established or academically accepted.