It would be neat to run their pdf through their implementation[1] and compare results.
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Suppose you get a paper, you automatically implement the code, and then modify it a bit with a novel idea, and publish your paper. Then somebody else does that with your paper, and does the same.. at some point, we will have a huge quantity of vibe coded code on github, and two similar papers will have very different underlying implementations, so hard to reason about and hard to change.
From a learning perspective, you try to understand the code, and it's all spaghetti, and you loose more time understanding the code than it would take to just reimplement it. You also learn a lot by not only reading the paper but reading the authors code where most of the small details reside.
And I'm not even talking about the reliability of the code, test to know that it's the correct implementation. Authors try to make papers as close as possible to the implementation but sometimes subtle steps are removed, sometimes from inadvertance, sometimes because the number of pages is lionmited.
A paper and an implementation are not one-to-one mappings
We should have the technology now to hand-write pseudocode on a piece of paper (or whiteboard or chalkboard), and have it translated and executed. Maybe you even hook up a projector, and project the output back onto the board
I agree with you in general, but maybe the jump would be similar to the one from hand-written punchcards/assembly to higher level compilers. Very few people worry about the asm generated from GHC for example. So maybe a lot of code would be like that. I also imagine at some point a better intermediate language for LLMs to generate will be discovered and suddenly that's how most programs will be written.
I would love to have a structured approach to help students learn to use better LLMs. What I have observed (for uni students) is that they produce better code overall, but have no idea how it works, and would not be able to reproduce it without LLMs. (this is from first to last year)
The example codes they give is 'implementing deep learning papers', I find those papers the easiest to implement compared to some obscure algorithm for example that can't rely on frameworks such as pytorch and where speed is critical.
I can't find the essay, but I think it was wolfram that wrote that we should let students use Mathematica and educate them in using it from a young age, the rationale behind is: before you had to use logarithmic tables, and it took much time during the education. Then, with the event of the calculator, students could instantaneously compute logarithms, so they could focus on more advanced ideas that use them. With Mathematica they could automatically execute matrix operations, so they would spend most of the time thinking about matrix operations instead of just learning how to manipulate a matrix by hand.
So with more powerful tools, you can expand the capabilities faster.
But the main difference I see here, is that maths are precise and well defined. Here you get a software which is a sample in the space of possible softwares that solve the problem (if you are lucky).
To get to the metaphorical point punchcards->GHC you need a LLM tool that give always the same answer, and hopefully, the optimal one, and with small changes in the paper, it moves the software in the space of viable softwares only a bit. Maybe we will get there, but this is not yet what this paper proposes