It's not really a capability, it's more like a very costly hack and they make that very clear in the paper. Training two models (an encoder and a decoder) for the purpose of explaining a single layer at a time is not that sensible. It's neat that you can generate so much readable text about how the LLM decodes partial input, and I suppose it gives you some extra debugging ability, but that's all there is to it.
The NLA also hallucinates, so it's still not revealing the models actual "thoughts" of the model; The paper also points out that since the NLA is a full LLM, it can make inferences that aren't actually in the activations.
But it's a useful approximation for auditing.