You’re right - poor compression can cause that. But skipping compression altogether is also risky: once context gets too large, models can fail to use it properly even if the needed information is there. So the way to go is to compress without stripping useful context, and that’s what we are doing

That's why give the chance to the model to call expand() in case if it needs more context. We know it's counterintuitive, so we will add the benchmarks to the repo soon.

Given our observations, the performance depends on the task and the model itself, most visible on long-running tasks