[kovek]

Well, the LLM may re-infer the whole state fully on every instruction. Temperature 0 is deterministic and that's what we are looking for. If the model is trained properly on how the CPU state + instructions should be handled, then it should be able to produce the next state.

[lossolo]

With temp = 0 if the model is off by one bit at step k, all subsequent steps are deterministically wrong.

Your previous example shows the best case, which is a model can sometimes follow a textual recipe for long multiplication on short inputs. That's not the same as learning a length generalizing bit exact algorithm.

Basically what you shown is the model can describe the algorithm. It doesn't show it can execute it at scale. Without writable state and bit exact ops, errors grow with length and "focus more" only slows that failure, it doesn’t eliminate it.

[kovek]

> It doesn't show it can execute it at scale. Without writable state and bit exact ops,

Well, modern LLM coding agent products (eg. Claude Code) are able to store state in files in the current repository. So, you could have the model keep the "CPU State", and the files in the repository be the "RAM".

Also, could this https://arxiv.org/html/2402.17764v1 possibly reduce errors when doing inference? There is no floating point operations

[razodactyl]

It seems to be the conclusion that we come to though, we ourselves use tools.

The focus here is the LLM being able to do it unaided.

The space of all combinations of steps is so large for many problems that require precision and usually one incorrect step breaks everything. "I forgot to carry the 1".

Even then, while brilliant, Claude does screw up sometimes - we're not there yet but it doesn't prevent it from being adequately useful.