The paper positions these purpose-built models, that explicitly decompose spatial reasoning tasks into sub-tasks, as better than these huge end-to-end models that do everything, at least in terms of interpretability and generalization. I am partial to that argument; my intuition is that the tighter the specification for a task, the better the model can be - because training objectives are clearer, data can be cleaner, models can be smaller, and so on. I feel like that is how my brain works, at least for more complex tasks. However, I do wonder if this is because I naively still want to be able to understand what the model is doing and how is does it, in a symbolic way - when that simply won't lead to the best empirical results.