Yeah, I think it's going to be a promising area of research. It's known as Chain-Of-Thought (CoT) in the literature and there are a few papers already out:

https://openreview.net/forum?id=_VjQlMeSB_J

https://openreview.net/forum?id=5NTt8GFjUHkr

https://openreview.net/forum?id=qFVVBzXxR2V

I'm also looking forward to explicit long-term memory mechanisms that don't rely on the main architecture's weights nor on CoT.

There's no memory or iteration log. You are just priming the (more or less deterministic even...) predictor with some history. You are doing a lot of work yourself, setting a factually correct context (and this has severe limits...).

As for "we just need a way to include iterations": isn't this what attention is supposed to do (kind of dynamically updating weights). The usual way to really update weights are variations on gradient descent. Can you link a paper outlining how we integrate your proposal into the current framework (which took around 20years to mature)? Otherwise your statements are Sci-Fi (at the flying cars-level).

Oh so there's no memory, just recallable history.

Oh so it's not learning by example, it's just primed by a sample.

Oh so there's no iteration of thought, just a loop of prediction.

OK.

Also, the papers you asked about: https://news.ycombinator.com/item?id=35115563

BTW, people are already implementing recursive/iterative queries on the ChatGPT API, and getting promising results.

The langchain project is an example of the iterative queries approach. It comes with constructs for working memory, factual lookup / calculation agents, etc.

https://github.com/hwchase17/langchain

The general (non-technical) guideline is that the LLMs can "answer" anything you just gave them the answer for. So you give it a problem, ask it how to solve it, tell it to use that method and explain the data it needs, give it that data, and then show it everything at once: "With this data you requested and summarized, use this technique to answer this question".