The recent developments in the research area of multi-robot systems and cooperative strategies have shown a significant shift towards decentralized and adaptive solutions, addressing various constraints such as communication, sensing, and movement limitations. A notable trend is the integration of advanced algorithms, including neural networks and quality diversity approaches, to enhance task allocation, patrolling strategies, and path planning in complex environments. These innovations aim to improve scalability, robustness, and efficiency in multi-robot operations, particularly in dynamic and resource-constrained scenarios. Additionally, there is a growing focus on system-level optimization, such as automated workspace design and sensor placement for localization, which underscores the practical application of these theoretical advancements. Notably, the field is witnessing a blend of theoretical analysis and empirical validation, ensuring that new methods are not only innovative but also reliable and effective in real-world settings. Papers that stand out include those proposing decentralized feedback strategies for simultaneous capture and novel algorithms for robust Euclidean distance geometry, showcasing significant theoretical contributions and practical implications.