The field of multi-objective optimization and verification is moving towards more efficient and effective methods for handling complex problems. Researchers are exploring new approaches to decompose complex problems into smaller subproblems, such as assume-guarantee reasoning, and developing new quality measures for evaluating solutions. There is also a growing interest in stochastic multi-objective optimization, with a focus on developing principled and tractable methods for ranking multivariate distributions. Additionally, statistical model checking is being extended to handle multi-objective Pareto queries, enabling the evaluation of multiple optimization objectives simultaneously. Notable papers in this area include: Picking a Representative Set of Solutions in Multiobjective Optimization, which introduces a new measure called directed coverage and analyzes the computational complexity of optimizing various quality measures. Center-Outward q-Dominance, which proposes a new relation for strong stochastic dominance in multi-objective optimization and develops an empirical test procedure based on q-dominance.