Reasoning models reason well, until they don't

I find that they know what they know fairly well, but if you move beyond that, into what can be reasoned from what they know, they have a profound lack of ability to do that. They are good at repeating their training data, not thinking about it.

The problem, I find, is that they then don't stop, or say they don't know (unless explicitly prompted to do so) they just make stuff up and express it with just as much confidence.

I think a good test of this seems to be to provide an image and get the model to predict what will happen next/if x occurs. They fail spectacularly at Rube-Goldberg machines. I think developing some sort of dedicated prediction model would help massively in extrapolating data. The human subconscious is filled with all sorts of parabolic prediction, gravity, momentum and various other fast-thinking paths that embed these calculations.

Most amazing is asking any of the models to draw an 11-sided polygon and number the edges.

I asked gpt5, and it worked really well with a correct result. Did you expect it to fail?