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I don't really see the relationship to your comment and the paper's content. Could you elaborate a little?

It's the last line of the abstract.

> As a consequence of this succinctness, we show that basic verification problems for transformers, such as emptiness and equivalence, are provably intractable: specifically, EXPSPACE-complete.

That's saying you can't formally verify an LLM, not that LLMs can't be used in formal verification.

But, if I have understood correctly on a quick read, they also claim transformers have pretty low expressive power. In particular, they claim they are limited to star-free subregular languages, whereas RNNs can recognize any regular language/simulate finite automata.

This doesn't imply you can't get aid from a LLM to e.g. implement a function that has a formal specification (an application I think is very promising), but surely it has some profound implications on how much of a large system can be understood by a LLM at once, without supervision.

Good news! On the current trajectory, I am very hopeful nonlinear RNNs could make a comeback. Which would incidentally help to ease the memory pressure for inference tasks.

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