New framework explains why LLM self‑play stalls and how to break out
The paper shows that iterative feedback loops in LLMs, reinforcement learning, and autonomous discovery quickly hit a stability ceiling, limiting further gains. It introduces a three‑level operational model that treats structural changes as falsifiable interventions, offering concrete metrics for detecting and escaping saturation—directly useful for anyone building LLM self‑play or recursive improvement systems.