Imagine a memory-augmented transformer model that needs to compress its long-term context to manage memory usage. One proposed strategy is 'uniform compression,' where all past information, regardless of how old it is, is compressed at the same fixed rate. Contrast this uniform approach with a 'differential compression' strategy, where older information is compressed more heavily than more recent information. Analyze the potential trade-offs of the differential approach in terms of memory efficiency and model performance on tasks requiring long-range dependencies.