Having more context, but leaving open an inability to effectively focus on the latest task is the real problem.
The main thing is people have already integrated AI into their workflows so the "right" way for the LLM to work is the way people expect it to. For now I expect to start multiple fresh contexts while solving a single problem until I can setup a context that gets the result I want. Changing this behavior might mess me up.
That may be the foundation for an innovation step in model providers. But you can achieve a poor man’s simulation if you can determine, in retrospect, when a context was at peak for taking turns, and when it got too rigid, or too many tokens were spent, and then simply replay the context up until that point.
I don’t know if evaluating when a context is worth duplicating is a thing; it’s not deterministic, and it depends on enforcing a certain workflow.