Autoresearch for SAT Solvers

https://github.com/iliazintchenko/agent-sat

by chaisan • 4 months ago

167 25 4 months ago

ericpauley4• 4 months ago

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.

jmalicki• 4 months ago

Or for that matter even from later versions of the same solvers that were in its training data!

ericpauley4• 4 months ago

True. I’d be curious whether a combination of matching comp/training cutoff and censoring web searches could yield a more precise evaluation.

dooglius• 4 months ago

Is z3 competitive in SAT competitions? My impression was that it is popular due to the theories, the python API, and the level of support from MSR.

ericpauley4• 4 months ago

Funnily, this was precisely the question I had after posting this (and the topic of an LLM disagreement discussed in another thread). Turns out not, but sibling comment is another confounding factor.

throw-qqqqq• 4 months ago

Z3 is capable (it’s an SMT solver, not just SAT), but it’s not very fast at boolean satifiability and not at all competitive with modern SOTA SAT solvers. Try comparing it to Chaff or Glucose e.g.

stefanpie• 4 months ago

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.

[1] "Autonomous Code Evolution Meets NP-Completeness", https://arxiv.org/abs/2509.07367

chaisan8• 4 months ago

nice. EDA indeed one of the top applications of SAT

gsnedders• 4 months ago

What counts as “our cost”? How long it takes to find the MaxSAT?

chaisan8• 4 months ago

the sum of the weights of the unsatistied clauses. we want to reduce this number

ktimespi• 4 months ago

sounds like AlphaDev [1] might be a better approach for a problem like this.

[1] https://github.com/google-deepmind/alphadev

chaisan8• 4 months ago

somewhat

MrToadMan2• 4 months ago

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?

chaisan8• 4 months ago

yeh. ofc. but on any problem larger than 40 variables, the gains from random restarts or initializations will quickly plateau

chaisan8• 4 months ago

and it would take an algo change to the solver to jump to the next local optimum

cerved• 4 months ago

Would me be nice to try this on lcg (CP-SAT) solvers

CJefferson• 4 months ago

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.

chaisan8• 4 months ago

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

whatever12• 4 months ago

I don't understand why autoresearch is presented as a new thing.

It is parameter tuning. We have been doing it for centuries.

chaisan8• 4 months ago

sure. in the limit, everything is parameter tuning. with large enough NP-hard problems, the complexity of the search space is big enough that its infeasible to get to a better state by just tuning params in any reasonable amount of time.

whatever12• 4 months ago

I beg to disagree. Integer programming solvers have improved orders of magnitude in the past 20 years. The basic algorithm (branch and bound) is the same.

The big commercial solvers basically are very good at picking up structures and selecting the tuning parameters that work better for specific problem types.

jacklondon• 4 months ago

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.

Autoresearch for SAT Solvers

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