The field of combinatorial optimization is witnessing significant advancements with the development of novel algorithms and techniques. Researchers are exploring new ways to harness statistical structure and avoid local minima traps in stochastic local search, leading to improved performance in solving complex problems. Additionally, the integration of quantum computing and machine learning is enabling the creation of more efficient and scalable optimization methods. The use of higher-order neuromorphic Ising machines and autoencoders is showing promise in achieving state-of-the-art quality and reliability in solutions. Noteworthy papers include: Advancing Stochastic 3-SAT Solvers by Dissipating Oversatisfied Constraints, which introduces a new stochastic local search heuristic that outperforms existing solvers, and Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability, which reports a higher-order neuromorphic Ising machine that exhibits superior scalability and achieves state-of-the-art quality and reliability in solutions.