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I have just spent a month writing about 2000 lines of Forth. My answer is no, at least w/r to generating something that looks like the by-hand code I wrote. LLMs coast by on being able to reproduce idiomatic syntax and having other forms of tooling(type checkers, linters, unit tests, etc.) back them up.

But Forth taken holistically is a do-anything-anytime imperative language, not just "concatenative" or "postfix". It has a stack but the stack is an implementation detail, not a robust abstraction. If you want to do larger scale things you don't pile more things on the stack, you start doing load and store and random access, inventing the idioms as you go along to load more and store more. This breaks all kinds of tooling models that rely on robust abstractions with compiler-enforced boundaries. I briefly tested to see what LLMs would do with it and gave up quickly because it was a complete rewrite every single time.

Now, if we were talking about a simplistic stack machine it might be more relevant, but that wouldn't be the same model of computation.

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> It has a stack but the stack is an implementation detail, not a robust abstraction.

Not exactly. Not only the stack is central in the design of Forth (see my comment over there [1]).

It seems to me that a point-free language like Forth would be highly problematic for an LLM, because it has to work with things that literally are not in the text. I suppose it has to make a lot of guesses to build a theory of the semantic of the words it can see.

Nearly every time the topic of Forth is discussed on HN, someone points out that the cognitive overload* of full point-free style is not viable.

[1] https://news.ycombinator.com/item?id=46918824#46921815

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I didn't finish my second sentence: not only the stack is a central element in the design of Forth, it also has 2 of them: data stack and return stack. the return stack is also used directly by programmers.
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Most models are multi-paradigm, and so they get... Fixated on procedural language design. Concepts like the stack, backtracking, etc. violate the logic they've absorbed, leading to... Burning tokens whilst it corrects itself.

This won't show up in a smaller benchmark, because the clutching at straws tends to happen nearer to the edge of the window. The place where you can get it to give up obvious things that don't work, and actually try the problem space you've given.

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I haven’t tried the extremes. Context rot says it’ll likely degrade there anyway.

What I’m investigating is if more compact languages work for querying data.

What makes you think it’s going to clutch at straws more? What makes you think it won’t do better with a more compact, localized representation?

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Even though I really like postfix from an elegance standpoint, and I use an RPN calculator, IMO it's harder to reason about subexpressions with postfix. Being able to decompose an expression into independent parts is what allows us to understand it. If you just randomly scan a complex expression in infix, if you see parenthesis or a +, you know that what's outside of the parenthesis or on the other side of a + can't affect the part you're looking at.

If you're executing the operations interactively, you're seeing what's happening on the stack, and so it's easy to keep track of where you are, but if you're reading postfix expressions, it's significantly harder.

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I find some calculations easier to reason about using either RPN or algebraic. Its entirely context driven.

Playing with APL has really changed the way I look at both.

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Probably prefix notation would work better, but I suspect that there would be a stronger effect from predictable and reasonable declinations/suffixes/prefixes at a grammatical level.
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The claim seems extremely unlikely to me. LLM comprehension is very sophisticated by any metric, the idea that something as trivial as concatenative syntactic structure would make a significant difference is implausible.

LLMs handle deeply nested syntax just fine - parentheses and indentation are not the hard part. Linearization is not a meaningful advantage.

In fact, it’s much more likely to be a disadvantage, much as it is for humans. Stack effects are implicit, so correct composition requires global reasoning. A single missing dup breaks everything downstream. LLMs, and humans, are much more effective when constraints are named and localized, not implicit and global.

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I’m not claiming forth should be used as is. I’ve opened the benchmark so others can reproduce the result I share in the post: https://github.com/rescrv/stack-bench
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