[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!