It should be noted that MaxSAT 2024 did not include z3, as with many competitions. It’s possible (I’d argue likely) that the agent picked up on techniques from Z3 or some other non-competing solver, rather than actually discovering some novel approach.
Prof. Cunxi Yu and his students at UMD is working on this exact topic and published a paper on agents for improving SAT solvers [1].
I believe they are extending this idea to EDA / chip design tools and algorithms which are also computationally challenging to solve. They have an accepted paper on this for logic synthesis which will come out soon.
Not as many changes to the files under library as I expected to see. Most changes seemed to be under a single ‘add stuff’ commit. If some of the solvers are randomised, then repeatedly running and recording best solution found will continually improve over time and give the illusion of the agent making algorithmic advancements, won’t it?
One problem here is it's very easy to overtune to a past problem set -- even accidentally. You can often significantly improve performance just by changing your random number generator seed until you happen to pick the right assignment for the first few variables of some of the harder problems.
It would be interesting to take the resulting solver and apply it to an unknown data set.
yess. loads of space for further exploration here. there is an attempt to keep things as general as possible in the expert.md file, but hard to mitigate overfitting fully. however, changing the seed will not get you much further with all else in the solver constant. unless you try a number of seed that exponentially scales with the size of the problem
Very interesting. For me the key question is whether this kind of agent can generalize to real SAT application domains, not only benchmark instances. In problems like timetabling, encoding choices, auxiliary variables, and branching strategy can matter a lot. If it can help there too, this is a very meaningful direction.
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[ 2.8 ms ] story [ 43.5 ms ] threadI believe they are extending this idea to EDA / chip design tools and algorithms which are also computationally challenging to solve. They have an accepted paper on this for logic synthesis which will come out soon.
[1] "Autonomous Code Evolution Meets NP-Completeness", https://arxiv.org/abs/2509.07367
[1] https://github.com/google-deepmind/alphadev
It would be interesting to take the resulting solver and apply it to an unknown data set.
It is parameter tuning. We have been doing it for centuries.