A bigger window solves the fitting problem. What solves the other three?

Context windows are at a million tokens now, Llama 4 Scout is at 10 million, and Magic has a 100 million token model. That's genuinely great and I want it to keep going. What I'm less sure about is the assumption that tends to come attached to it, that memory stops mattering once the window is big enough. To me they read as two separate investments rather than one replacing the other. A bigger window solves the fitting problem, and that's a real problem worth solving. But the session still ends and the next one still starts at zero, accuracy still drops as you add more, and you still pay to reprocess the same context every time. Chroma's context rot work tested 18 models and found performance degrading well before the actual limit, with even a single irrelevant distractor measurably hurting accuracy, so fitting in the window and using the window well aren't quite the same thing. Which is why I think memory has to keep improving on its own, through things like recency handling and some signal of confidence on what it hands back, rather than waiting for the window to grow into it. Curious where people land on this, and what's actually breaking for you right now.