Integration of Supercubing and Learning in a SAT Solver Domagoj Babic and Alan J. Hu IEEE Asia South Pacific Design Automation Conference, IEEE Press, 2005, pp. 438-444. Learning is an essential pruning technique in modern SAT solvers, but it exploits a relatively small amount of information that can be deduced from the conflicts. Recently, a new pruning technique called supercubing was proposed. Supercubing can exploit functional symmetries that are abundant in industrail SAT instances. We point out the significant diffculties of integrating supercubing with learning and propose solutions. Our experimental solver is the first supercubing-based solver with performance comparable to leading-edge solvers.