Oh? I've had great luck with LLMs and homemade ILs. It has become my favourite trick to get LLMs to do complex things without overly complicating my side of the equation (i.e. parsing, sandboxing, etc. that is much harder to deal with if you have it hand you the code of a general purpose language meant for humans to read).
There is probably some point where you can go so wild and crazy with ideas never seen before that it starts to break down, but if it remains within the realm of what the LLM can deal with in most common languages, my experience says it is able to pick up and apply the same ideas in the IL quite well.
It is trained on WASM btw, but if we invented one specific for it, it could easily be trained up on it or refined with it. I've already had some success just handing it a language guide and it runs with it.
People are still confusing AI putting together scraps of text it has seen that correlates with its understanding of the input, with the idea that AI understands causation, and provides actual answers.
And people are also still clearly confusing "isn't human or conscious" with "can't possibly create new logical thoughts or come to new logical conclusions i.e. do intellectual labor" when there is a plethora of evidence at this point that the latter is, in fact, the truth
I'm not sure if you mean that as a dig, or not, but if you are referring to me then I have these data points to discuss.
1. I have encountered a problem where AI will suggest 4 different "solutions" and when I point out a problem with one, it cycles on to the next, and stays in that loop, repeating over and over that set of 4, with no recollection of the previous refutation of the soltuion (this is a mix of context retention, and the fact that the solution selection is limited to that which has already been fully explored on the web - I had a 5th idea in mind which the AI failed to understand, but worked well)
2. Yesterday I was discussing with AI the fact that I had three options for action, and it misunderstood that as 4 actions, a trivial arithmetic failure.
This demonstrates (clearly) that the AI didn't "understand" the points discussed, and was instead staying with the correlation of text with other text.
I really like where AI is at the moment and use it a lot - it's very helpful for debugging, for example, but as every vibe coder out there will attest, AI fails hard at standalone coding, and I submit that this is a symptom of its inability to understand what its doing.
It's still correlation is not causation, and it demonstrates why correlation is so attractive, you can get quite far knowing that there is a correlation between ice cream sales and shark attacks, but it takes work to understand that there is no causative link (FTR I suspect that it's because ice cream sales go up in hot weather, more people are in the ocean during those hot weather periods, therefore there's more opportunity for people to interact with sharks)
Edit: Note how I use the word "suspect" when I talk about the cause of the correlation - it's VERY tempting to say that the weather is the cause, but that's still a correlation, and the fact is, as humans have discovered, actual research is required to verify whether that is, indeed, the cause, or not - something AI might miss.
