2025/04/26 by Haoze Wu, Clark Barrett, Wu, Haoze +3 · 1 citation
Engineering · #Adhesion, Friction, and Surface Interactions #Advanced Surface Polishing Techniques #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Material Properties and Processing
paper · pdf · doi:10.48550/arxiv.2504.19039
openalex publication_date 2025/04/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.