They dont. They have input that runs through a invisible stochastic canyon. As long as there is previous experience the stochastic canyon never ends. If there is none or isignificant one, or it runs out of tokkens, it hallucinates and the illusion falls apart. There is no reasoning, just the invisible grand canyon of all of human experience and knowledge. PS: try to get it to retell you a clichee movie or book and you can see life near the end, how the delta of all the same movies opens up into wildly different endings.

To advance further it would need the ability to abstract away the general situation shape and pattern recognize similar situations.

With that definition, computers don't play chess, they just move the pieces using some weights and backtracking.

When a mathematician reads a hundred-year-old math paper, it seems like they are reproducing in their head the reasoning of someone who died long ago. That is, reasoning can be written down and replicated.

If that works, I think it's fair to say that LLM's are inanimate processes can generate real reasoning. You can tell when you read it and it makes sense.

There are likely some kinds of reasoning that can't be written down, as well as other forms of understanding, but they also don't replicate nearly as easily.

It's probably helpful in this discussion to make a difference between two definitions of reasoning:

1. phenomenal reasoning, requiring consciousness and subjective experience

2. functional reasoning, transforming premises into conclusions using logic

I think you are attacking this using definition 1, whereas the article is obviously aiming at a different type of reasoning, and trying to formalize what is actually going on. It seems to be a genuine effort.

Compression is the trick. Its even philosophed about if compression = intelligence.

The LLM has to compress everyy question/prompt into its system. It does so by creating rules and ways of processing data (this can lead to AGI, world models or an architecture of sub architectures like an LLM + something else). So if it should respond in a way that only reasoning people can achieve, it might be able to learn a representation of what we call reasoning.

It read enough text in itself to even know about the concept of reasoning and how you would do that.

Even if this is only stochastic, it shouldn't be so devalued as your comment comes across.

Who says that we are doing anything more magic?

Stochastic gradient descent can be likened to traveling down a billion-dimensional canyon. But inference? Hardly.

It’s curious how they solve unsolved math problems without reasoning. Maybe I have a different definition of reasoning than you.

There is a streamer who plays Diablo 2 by listening to the AI advice and it is quite funny since it is pretty clear that most of the advice is an amalgamation of random, often incorrect advicem

I wonder if it is the same for programming or not, but I vibe coded an android app just to see if I can and it just works. It required a lot of "build the code and correct the errors" pushing though. For example requested code in kotlin but received something else.