A statistical approximation of logical inference (as vague as I state it) could (and will) very well pass for logical inference, at least for the common people, whose logic skills are far from perfect.

Also, humans are certainly not capable of the perfect logical inference you speak of. And I get the irony of what I'm saying with such certitude. Logic is still framed in axioms that are framed in languages, we'll never truly get there. Ah, but absoluteness gets in the way of practicality.

Yet, here we are with a tool, that is maybe not at its prime yet, that equals and beat many human beings at logical inference on some problems that are pragmatically relevant. Should I say symptoms of logical inference at that point?

As to why LLMs capacity for (apparent) logical inference is only limited to specific use cases, I don't have a clue. But I'd like to argue that, humans are like that too.

Funny statement to be found in the discussion about... research results on the fundamentals.

If you have a tool that you don't know works when data increases (n-> infinity), then you shouldn't use it.

So practicaly, I believe it has serious implications.

Shor's and Grover's still are algorithm that require a massive amount of steps...

What's the loop behind consolidation? Random sampling and LLM to merge?

I have not taken the time to review the paper, but if the claim stands, it means we might have another tool to our toolbox to better understand transformers.