Maybe in conjunction with a deterministic decompiler.

precision wrt translation, especially when the translation is not 1-to-1, is not excellent with LLMs.

In fact, their lack of precision is what makes them so good at translating natural languages!

It's also perfect for RL because it can compile it's output and check it against the input. It's a translation exercise where there's already a perfect machine translator in one direction.

It probably just hasn't happened because decompilation is not a particularly useful thing for the vast majority of people.

Let me parrot you, it's fun.

"It's pattern matching, plain and simple, an area where pattern matching algorithms excel. Pattern matching driven decomp absolutely leads"

Decompilation is a dependence graph problem, one can formulate decompilation as a graph transformation/rewrite. Neural networks are notoriously bad at graphs.

> Neural networks are notoriously bad at graphs.

AlphaFold is based on graph neural networks. The biggest issue is that we still do not know how to best encode graph problems in ways neural networks can exploit. Current graph neural network techniques exploit certain invariants but cannot distinguish between various similar graphs. And yet, they're still generating meaningful insights.

> AlphaFold is based on graph neural networks.

And what is the size of graphs processed by AlphaFold? How does it compare to the size of program dependence graph?

Here's evaluation of boolean satisfiability encoding of protein folding problem: https://pmc.ncbi.nlm.nih.gov/articles/PMC7197060/

Boolean satisfiability solvers are used in satisfiability modulo theories solvers that, in turn, are used to prove various things about programs.