PLUME search is a data-driven framework that improves search efficiency in combinatorial optimization problems through unsupervised learning. Unlike supervised learning or reinforcement learning, it uses a non-autoregressive approach to learn directly from problem instances via a permutation-based loss function. In this paper, we evaluate its performance on the quadratic assignment problem, a fundamental NP-hard problem encompassing various combinatorial optimization problems. Experimental results demonstrate that PLUME search consistently improves solution quality. We also investigate whether the learned model generalizes to different densities and sizes.
