> I'm building a language with AI, and when I leave the AI running on its own, it keeps generating code in strange directions.

In the linked paper the researchers state "AI solutions must match the original program’s output exactly on end-to-end tests, including held-out tests. " And a bit further on "The AI agent is given execute-only access to the original program and a set of visible test cases, but does not have access to the original source code. "

At a guess, their agents are not going off into the weeds (like mine do occasionally) is because they have both access to a reference runtime implementation to use as a definitive end to end test to guide development. And also a relatively large token budget to run enough characterisation tests to allow them to home in on solutions.

Also building a language with AI.

It's decent at design and implementation, but strangely terrible at language consistency - and expectedly architecture.

Steering it on language consistency is easy. Steering it on architecture has required me to learn a lot more about compilers than I wanted to do for this project (since that's a lot more than I wanted to take on for something that was supposed to be a "fun" "side" project).

Is it like self driving cars, where 100% non-human code would be arguably safer?

But then it always comes down to having some human who is accountable for verifying the behavior. I think ultimately we just need a better set of languages and frameworks to minimally encode the types of applications we want to build today, so that human comprehension and verification of the source code becomes easier.