SILENCIUM III: Synthetic Negative Anchoring as a Conceptual Defense Against Recursive Model Degradation

This working paper proposes SILENCIUM III, a conceptual framework for Synthetic Negative Anchoring as a risk signal against recursive model degradation in language-model systems. The approach maps known families of low-variance synthetic artifacts, repetitive model-generated patterns, and hallucination signatures into a negative reference space. Candidate data, prompts, and generated outputs can then be evaluated against this space to support training-data curation, prompt routing, and post-generation plausibility checks. The paper does not claim empirical validation or general AI-text detection. It presents a falsifiable system architecture intended to complement source grounding, intent gating, and epistemic integrity controls within the broader SILENCIUM framework.