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It recognized it as a "famous puzzle" in its thinking trace. However, I suspect that the common version of the puzzle doesn't account for curvature. I tried looking for it, but didn't find anything, but similar variants (often seen in aptitude or IQ tests) implicitly assume a flat surface.
In fact, on double checking, the model knows that the classic form assumes a flat map. It specifically decides to answer it in more depth.
The most common failure mode in LLM skeptics (and I don't mean to use that phrase to describe people who don't believe that LLMs are AGI, or that they have clear flaws) is to assume that all improvements come from intentional efforts by AI companies to hastily patch such flaws. It's not that this doesn't happen, but it's usually in the context of benchmark maxxing by the less scrupulous companies (and occasionally, when the PR hit is strong enough, they'll add specific instruments, such as the "Rs in strawberry" one, which was specifically addressed in Claude's system point a while back).
The issue with this approach is that it leads to maximal paranoia and complacency, and as excuse to dismiss clear and obvious improvements in all domains. And even in the worst case, patching specific failure modes is still an improvement. LLMs are supposed to suffer on truly "out of distribution" problems (I have my reservations, I wonder how the average human fares) but in principle, if you can actually capture most of that distribution, you've got something that is effective in deployment (though it might be brittle, but once again, we're talking about a hypothetical model that is actually trained on nearly everything).
Finally, I really doubt that OpenAI or Anthropic went to the trouble of patching this specific puzzle on purpose. They didn't even hard code the strawberry example, they just hinted to the model that it suffers from tokenization problems, and that it should try and use code to check instead of parsing it itself (a defensible position). They didn't, as far as I can tell, patch the far more famous "but I can't operate, the boy is my son!" trick question, and it was tripping up the best LLMs for years. I suspect it might do so today.
In other words, if you're famous for maintaining some kind of formal benchmark, it might be worth their while to artificially target your questions. They have better things to do in general, for smaller problems like this.
At a minimum, there's reason to be skeptical, to think that there is a lot of "hasty patching" going on.
I will go out on a limb and guess that within a few days all of the major LLMs will give the correct answer to the carwash problem. Which, if true, is quite a coincidence.
I don't dispute that LLMs are improving by leaps and bounds, but I still think there's a good chance that something vital is missing. If that's "madness," then so be it.
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@omw_68
I tried getting GPT 5.2T to look for examples:
Lol. Lmao. I suppose Google or Bing has very fast crawlers?
Indeed. I would explain the team I once worked at Google to people sometimes as "Did you ever post on some forum looking for the answer to a question, and then decide to search for it, and the first result that came up was your own question? That's us."
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