My bandaid fix for this is having a large number of sessions or "agents" and being ok with them being inactive at times, but they maintain their context. This has to be in some kind of system with appropriately sized "departments" to justify that agent's existence, or to justify it splitting into two, or creating sub-agents, etc. Yes, there is a cost when you cold start an agent after a week, but for now I'd rather pull that whole 400k+ context back without layers and layers of /compact. And then hopefully you keep that agent going for long enough to benefit from any cache-hit discounts.

Not really a good analogy, but it feels like having an image you like but every time you view it, it runs it through another loop of jpg compression. Use it when it's most effective, otherwise let it preserve it's details.

And as I see it that is impossible with current autoregressive global attention models because each part depends on the one before. Maybe if we can cache the local attention in blocks and only recompute the global parts.