One approach is to have the model learn to select between several separately fine tuned adapters by learning which adapter works best in a given context. So at any given time it's only really using one adapter but can switch to another. In this case one adapter can't really improve another but the overall impact might be a model which is improved in a variety of different contexts.

Yes, but the naïve way to combine rank k adaptations created by n different people would be to concatenate them to a rank nk adaptation, which wouldn't be as lightweight and easy to share, so you'd likely be better off mushing them into the baseline model.