If LLMs really were to "think" in the broader sense, and not pattern match on a huge database, adversarial features shouldn't pose a problem to them. E.g. humans are naturally bad at doing math. Barring some exceptional humans, we derived mechanisms to get math results that we can't easily do in our head on paper.

If the LLMs were thinking in the sense that some people say they are, they will easily recognize the adversarial nature (e.g. letter counting in the language) and build their own tools to write conformant code right away. As a normal human would also build tools to solve the things we know we're not naturally good at.

The effect should be that a few tokens are spent on building the tool, and then the rest of the problem can be solved with a "normal" amount of tokens without brute-forcing the solution.

If the LLMs are glorified pattern matchers they might not find enough patterns in the corpus of human knowledge and remain inefficient at it.

The weakness I see is rather that this kind of problem-solving can be pattern matched on the corpus of existing human knowledge, so it may be that pattern matching on human thinking might still be enough to solve these problems.