I have been planning to work on something like this. I think that eventually, someone will crack the "binary in -> good source code out of LLM" pipeline but we

I have been planning to work on something like this. I think that eventually, someone will crack the "binary in -> good source code out of LLM" pipeline but we are probably a few years away from that still. I say a few years because I don't think there's a huge pile of money sitting at the end of this problem, but maybe I'm wrong. A really good "stop-gap" approach would be to build a decompilation pipeline using Ghidra in headless mode and then combine the strict syntax correctness of a decompiler with the "intuition/system 1 skills" of an LLM. My inspiration for this setup comes from two recent advancements, both shared here on HN: 1. AlphaGeometry: The Decompiler and the LLM should complement each other, covering each other's weaknesses. https://deepmind.google/discover/blog/alphageometry-an-olympiad-level-ai-system-for-geometry/ https://deepmind.google/discover/blog/alphageometry-an-olymp... 2. AICI: We need a better way of "hacking" on top of these models, and being able to use something like AICI as the "glue" to coordinate the generation of C source. I don't really want the weights of my LLM to be used to generate syntactically correct C source, I want the LLM to think in terms of variable names, "snippet patterns" and architectural choices while other tools (Ghidra, LLVM) worry about the rest. https://github.com/microsoft/aici https://github.com/microsoft/aici Obviously this is all hand-wavey armchair commentary from a former grad student who just thinks this stuff is cool. Huge props to these researchers for diving into this. I know the authors already mentioned incorporating Ghidra into their future work, so I know they're on the right track.