Learning by Surprise: Surplexity for Mitigating Model Collapse in Generative AI

Cited in riva2026task as concrete evidence for the niche-construction feedback loop: surplexity work documents performance and diversity decay across generations when later models train on synthetic data that no longer surprises them. The cite supplies an empirical instance of the long-run veridicality concern.