Model‑Indexed Epistemic Collapse: A Framework for Measuring Knowledge Degradation in Large Language Models

This essay introduces a diagnostic framework for detecting and measuring epistemic degradation in large language models. Rather than treating model drift, hallucination, or factual decay as isolated failures, the work conceptualizes them as symptoms of a broader phenomenon: model‑indexed epistemic collapse. The framework maps how knowledge representations erode across training cycles, deployment contexts, and system updates, revealing the structural forces that destabilize model‑level epistemic integrity. By foregrounding the infrastructural conditions that shape model behavior—dataset volatility, alignment interventions, compression strategies, and platform‑level governance—the essay reframes degradation as an indexable, measurable process rather than an anecdotal complaint. It proposes a set of evaluative lenses for tracking how models lose, distort, or overwrite knowledge over time, offering a methodological foundation for future empirical research. This contribution extends the SignalRupture canon’s ongoing investigation into systemic erosion, infrastructural exposure, and the fragility of contemporary knowledge systems. It positions epistemic collapse not as a failure of individual models but as a structural property of the environments that produce, maintain, and continuously modify them.