Consider a language model architecture designed to handle very long sequences. Instead of a single memory buffer for past information, it uses two distinct buffers:

1. A 'local context' buffer that stores the most recent sequence of events in full detail.
2. A 'compressed history' buffer that stores a summarized, lower-resolution version of older events.

When calculating the next output, the model's attention mechanism is given access to the combined contents of both of these buffers simultaneously. Evaluate the potential advantages and disadvantages of this dual-memory approach for the attention mechanism compared to a system that uses only a single, fixed-size buffer for local context.