[jules]

The type of reasoning by the OP and the linked paper obviously does not work. The observable reality is that LLMs can do mathematical reasoning. A cursory interaction with state of the art LLMs makes this evident, as does their IMO gold medal scored like humans are. You cannot counter observable reality with generic theoretical considerations about Markov chains or pretraining scaling laws or floating point precision. The irony is that LLMs can explain why that type of reasoning is faulty:

> Any discrete-time computation (including backtracking search) becomes Markov if you define the state as the full machine configuration. Thus “Markov ⇒ no reasoning/backtracking” is a non sequitur. Moreover, LLMs can simulate backtracking in their reasoning chains. -- GPT-5

[godelski]

  > The observable reality is that LLMs can do mathematical reasoning

I still can't get these machines to reliably perform basic subtraction[0]. The result is stochastic, so I can get the right answer, but have yet to reproduce one where the actual logic is correct[1,2]. Both [1,2] perform the same mistake and in [2] you see it just say "fuck it, skip to the answer"

  > You cannot counter observable reality

I'd call [0,1,2] "observable". These types of errors are quite common, so maybe I'm not the one with lying eyes.

[0] https://chatgpt.com/share/68b95bf5-562c-8013-8535-b61a80bada...

[1] https://chatgpt.com/share/68b95c95-808c-8013-b4ae-87a3a5a42b...

[2] https://chatgpt.com/share/68b95cae-0414-8013-aaf0-11acd0edeb...

[FergusArgyll]

Why don't you use a state of the art model? Are you scared it will get it right? Or are you just not aware of reasoning models in which case you should get to know the field