Has Anyone Measured How LLM Output Quality Degrades Across Multiple Compactions?

The article delves into an intriguing observation about the performance of the DeepSeek V4 language model after multiple context compactions. The author noticed a temporary boost in output quality after the second compaction, followed by a sharp decline that doesn't recover. This raises questions about whether there's an undiscovered pattern in how context compaction affects model performance. This matter is significant because understanding these nuances could lead to better strategies for managing large language models and their outputs, potentially improving efficiency and accuracy in AI applications.