>making basic recall mistakes at <100k tokens.

I usually see this when the context gets "tainted" as I call it. The model gets stuck on a bad path and there's no way to bring it back without clearing the context and starting again.

Frequently it'll be something as small as 1 sentence of a prompt many messages ago.

When cases like that happen, I reset the context and try to be explicit about assumptions and requirements to keep it off the "tainted" path. Other times it's actually useful and agents will do things they normally wouldn't do once the state is tainted. For instance, if you're testing a chat bot's ability to stay on topic, you can seed the context early with what you want it to do. It generally will refuse initially but later on in the conversation it will still silently take that seeded context into account almost "subconsciously" and become more likely to do the thing it originally refused.