Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations

ArXi:2604.15448v1 Announce Type: cross Foundational optimization embeddings have recently emerged as powerful pre-trained representations for mixed-integer programming (MIP) problems. These embeddings were shown to enable cross-domain transfer and reduce reliance on solver-generated labels. In this work, we investigate whether such representations generalize beyond optimization to decision problems, focusing on Boolean satisfiability