# Cubing for Tuning

arXiv: https://arxiv.org/abs/2504.19039 · HTML: https://mlsanity.com/paper/2504.19039/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2025.

## Built on by (most cited first, 1 of 1)

- [The Luna Bound Propagator for Formal Analysis of Neural Networks](https://mlsanity.com/paper/2603.23878/) (2026)

## Abstract (arXiv)

We are exploring the problem of building an automated reasoning procedure that adaptively tunes the high-level solving strategy for a given problem. There are two main distinctive characteristics of our approach: tuning is performed solely online, unlike the common use of tuning as an offline process; and tuning data comes exclusively from the given instance, so we do not rely on the availability of similar benchmarks and can work with unique challenging instances. Our approach builds on top of the divide-and-conquer paradigm that naturally serves partitioned sub-problems for an automated tuning algorithm to obtain a good solving strategy. We demonstrate performance improvement on two classes of important problems--SAT-solving and neural network verification--and show that our method can learn unconventional solving strategies in some cases.