Thanks for the link! I am not familiar with the company but reminds me of the whole formal methods debate in distributed systems. Sure, writing TLA+ specs is the 'correct' deterministic way to build a Raft implementation, but in reality everyone just writes messy Go/Java and patches bugs as they pop up because its faster.
Neat, thanks for the link! Seems like the agent feedback loop made a big difference for your use case. Would be interesting to see how much of a difference more advanced methods (like the genetic algs you mentioned) would make
We've found that these frameworks do well for systems performance problems and expect that the range of problems for which they apply will increase as they models and frameworks improve. See our paper (https://arxiv.org/pdf/2510.06189) for more discussion about this
That's a good point! The load balancing of the original algorithm was already quite good so our goal was to try to get something that could achieve similar results but could run faster since runtime was also a concern.
Thanks for sharing your blog! Very interesting work, 100% agree with your 3 criteria on the sweet spot for AI. Most systems performance problems fit right in
The code was quite short and easy to read. Specifying the right scoring function and scoping the problem are key parts of getting good results with ADRS.
This is a great question! By analyzing the logs of OpenEvolve with the full model outputs, we observed how the AI got its ideas (seemed to be pulling from literature in the space) and how it tried to apply them. So in some sense, it "reasoned" about how to get better algorithms. And we saw this process proceed systematically via the ADRS framework to converge to a significantly better algorithm
As an author of the blog, I'll note that this was one of the easiest applications of ADRS. Bowen, who was leading this effort, got things running within a day or two and the initial runs were with free Google credits! It was exciting to see how quickly these kinds of frameworks could be applied to real-world engineering and algorithmic challenges.