Current evolutionary LLM systems are limited because the problem is given as fixed — true innovation requires co-evolving the problem and the solution together.

7:23kind of lock in and and converge a little bit. So, we we're still converging though. So, we're still building systems that don't diverge forever. What are we losing? >> One one thing I find extremely important after having done the Shinka Evolve is um sort of this problem problem, right? So, um with all of these systems so far, maybe except for the AI scientist, which we can also talk about, the problem is

7:46given, right? So, you have an evaluator, you have a correctness checker, and you sample programs only on that single problem, right? But, oftentimes innovation for a specific problem might require first inventing a different problem, right? So, for example, I think in the matrix multiplication result that the Alpha Evolve people show, you can recursively apply sort of the algorithm to larger matrices. So, it's actually an important result, right?

8:13But, sort of automatically coming up with this reduction or like this, let's say, recursive nature of problem solving is something these systems right now not necessarily have built in intrinsically, right? So, I think going forward it's going to be really important to not only sort of do open-ended, let's say, optimization of solutions, but sort of do the co-evolution of problem and solution together in order to collect even more

8:37diverse stepping stones, and um to really kick off this this open-ended process. Cuz also to me, like one of the the the big life goals or achievements I would want to see is really having a process that can run not only for, let's say, a week or many weeks, but like for years even potentially, right? Collecting even more diverse, interesting stepping stones. >> Yeah, I spoke to Joel Lehman and he was

8:59talking about the knightian uncertainty, which is that machine learning algorithms are very good with unknown unknowns. And and in a sense, the unknown unknown is talking about these these stepping stones that might be useful later. And when we run these algorithms at the moment, it's the same with LLMs and reasoning systems, is that they're very, very good when we give them a specific thing. And what you're pointing to is we might

9:24need to invent new unrelated problems and find the solutions which might then be related to what we're trying to do. So, that feels like a bit of a catch-22 situation, right? So, we're saying, you know, circle packing. Here's my evaluation function and I want you to sort of diversify and then you know kind of and then converge towards the solution. It's just I had the same thought with