Somewhat related, the experiment ongoing at https://www.ecdsa.fail/ is fascinating: it's a competitive, leaderboard-style research challenge trying to optimise a quantum circuit for breaking ECDSA (specifically the elliptic-curve point addition in Shor's algorithm). It quickly surpassed a result announced by Google researchers last month. Now it's showing a 40% gain over Google's result.
I'm personally interested in this problem and it's a quite active research area right now.
My feeling is that the research is converging to what the paper claims, that the combination of two is the right way to do it and it's a matter of how you combine the two as part of the harness you built that makes the difference.
At the AID-Wild / ACM CAIS 2026 workshop that happened recently, there are plenty of examples in the accepted papers on that.
A great example is AI-PROPELLER: Warehouse-Scale Interprocedural Code Layout Optimization with AlphaEvolve. It uses AlphaEvolve and Vizier to evolve compiler code-layout heuristics. (https://arxiv.org/abs/2606.00131)
The combination approach jives well with my use of the models in a number of areas. I guide models to use best-in-class algorithmic approaches as available. (Eg using constraint solves for a particular problem where pure Monte Carlo rarely gives "in-bounds" data.)
I find it odd that frontier models often don't suggest the most powerful methods for crushing problems, but it may be that the training data doesn't actually have "good enough" experts on the problems I encounter. If the experts don't know about the best ways to solve the problem, they'll get dinged in training for even trying.
Their centaur idea[1] is interesting and quite straightforward. It should be fairly easy to implement using a coding agent for the LLM and the ask-and-tell interface in pycma[2].
Somewhat related, the experiment ongoing at https://www.ecdsa.fail/ is fascinating: it's a competitive, leaderboard-style research challenge trying to optimise a quantum circuit for breaking ECDSA (specifically the elliptic-curve point addition in Shor's algorithm). It quickly surpassed a result announced by Google researchers last month. Now it's showing a 40% gain over Google's result.
I'm personally interested in this problem and it's a quite active research area right now.
My feeling is that the research is converging to what the paper claims, that the combination of two is the right way to do it and it's a matter of how you combine the two as part of the harness you built that makes the difference.
At the AID-Wild / ACM CAIS 2026 workshop that happened recently, there are plenty of examples in the accepted papers on that.
A great example is AI-PROPELLER: Warehouse-Scale Interprocedural Code Layout Optimization with AlphaEvolve. It uses AlphaEvolve and Vizier to evolve compiler code-layout heuristics. (https://arxiv.org/abs/2606.00131)
The combination approach jives well with my use of the models in a number of areas. I guide models to use best-in-class algorithmic approaches as available. (Eg using constraint solves for a particular problem where pure Monte Carlo rarely gives "in-bounds" data.)
I find it odd that frontier models often don't suggest the most powerful methods for crushing problems, but it may be that the training data doesn't actually have "good enough" experts on the problems I encounter. If the experts don't know about the best ways to solve the problem, they'll get dinged in training for even trying.
Their centaur idea[1] is interesting and quite straightforward. It should be fairly easy to implement using a coding agent for the LLM and the ask-and-tell interface in pycma[2].
[1]: https://github.com/ferreirafabio/autoresearch-automl/blob/ma...
[2]: https://github.com/CMA-ES/pycma
TDLR: No.
That's not a very good tldr. The answer claimed in the paper is that the combination of the two is better than either alone.