It's not broken, it's just doing its best to cope with your situation. Missing lots of days means that there will be lots of cards where it's just not clear if you're going to recall them or not. Figuring that out becomes the priority, then the system is effectively back to normal - possibly with some missed cards that will have to be learned again. I'm not sure how one could do better than that.
> it's just not clear if you're going to recall them or not. Figuring that out becomes the priority
Presumably the priority ought to be (re)starting with a small subset of cards and gradually trickling the others back in. The algorithm needs to account for time spent by the given individual and adapt to changes in that over time.
I haven't used Anki for about a decade so I'm not familiar with the current state of things. At the time a major factor in my dropping it was that I found the algorithm to be more of a hindrance than a help.
Sure it is. It could grow as quickly as allowed for given the time invested by the user. That could mean a return to the full subset within the span of a single day, or it could mean many months. Perhaps even never. It all depends on time invested by the user going forward. Starving regular review for the sake of verification is an example of an algorithm failing when faced with the real world.
At minimum it is clear from what was said that (better) prioritization between conflicting goals is needed. That somewhat matches my own experience with it from years ago. The algorithm was simply not flexible enough to fit my own usage patterns. In other words I was not part of the target audience, which I found frustrating because I very easily could have been.
It's not clear to Anki because its model ("due" or "not due") and algorithm ("study the card that's been due the longest") are too simplistic. I know it can be done better because I built a system that does better with the same data by having a better model ("expected difficulty") with a better algorithm ("study the card which will have the highest long-term impact on difficulty").