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You are missing one important point.

Your network can learn some dataset very well. However, that doesn't say anything about how well it generalizes, and thus how useful your network is.

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Your point is a salient one. It would be useful if we could provide guarantees/bounds on generalization, or representation power, or understand how brittle a model is to shifts in the data distributions. Are these questions of the kind that are answered in part by the authors? I haven’t read the manuscript, but the title doesn’t indicate this is the aim of the research, but it indicates an eye to something much broader and vague (“learning”).

The title is bad on lots of levels but also doesn't match the article, and further less the original paper.

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