[HarHarVeryFunny]

If you forget the LLM implementation, fundamentally what you are trying to do here is first detect a bunch of features in the photo (i.e. fine-grain image captioning "in foreground a firepit with safety warning on glass, in background a model XX car parked in front of a bungalow, in distance rolling hills" etc) then do a fuzzy match of this feature set with other photos you have seen - which ones have the greatest number of things in common to the photo you are looking up? You could implement this in a custom app by creating a high-dimensional feature space embedding then looking for nearest neighbors, similar to how face recognition works.

Of course an LLM is performing this a bit differently, and with a bit more flexibility, but the starting point is going to be the same - image feature/caption extraction, which in combination then recall related training samples (both text-only, and perhaps multi-model) which are used to predict the location answer you have asked for. The flexibility of the LLM is that it isn't just treating each feature ("fire pit", "CA licence plate") as independent, but will naturally recall contexts where multiple of these occur together, but IMO not so different in that regard to high dimensional nearest neighbor search.

[simonw]

Thanks, that's a good explanation.

My hunch is that the way the latest o3/o4-mini "reasoning" models work is different enough to be notable.

If you read through their thought traces they're tackling the problem in a pretty interesting way, including running additional web searches for extra contextual clues.

[HarHarVeryFunny]

It's not clear how much the reasoning helped, especially since the reasoning OpenAI display is more post-hoc summary of what it did that the actual reasoning process itself, although after the interest in DeepSeek-R's traces they did say they would show more. You would think that potentially it could do things like image search to try to verify/reject any initial clue-based hunches, but not obvious whether it did that or not.

The "initial" response of the model is interesting:

"The image shows a residential neighborhood with small houses, one of which is light green with a white picket fence and a grey roof. The fire pit and signposts hint at a restaurant or cafe, possibly near the coast. The environment, with olive trees and California poppies, suggests a coastal California location, perhaps Central Coast like Cambria or Morro Bay. The pastel-colored houses and the hills in the background resemble areas like Big Sur. A license plate could offer more, but it's hard to read."

Where did all that come from?! The leap from fire pit & signposts to possible coastal location is wild (& lucky) if that is really the logic it used. The comment on potential licence plate utility, without having first noted that a licence plate is visible is odd, seemingly either an indication that we are seeing a summary of some unknown initial response, and/or perhaps that the model was trained on a mass of geoguessing data where photos were paired not with descriptions but rather commentary such as this.

The model doesn't seem to realize the conflict between this being a residential neighborhood, and there being a presumed restaurant across the road from a residence!