The field of multi-agent systems and decision making is witnessing significant developments, with a focus on improving the scalability and applicability of existing models to real-world problems. Researchers are exploring novel approaches to address the challenges of non-stationary dynamics, large-scale systems, and complex interactions. One notable direction is the integration of deep reinforcement learning with mean field games, which has shown promise in modeling and solving complex multi-agent problems. Another area of interest is the development of adaptive and efficient algorithms for computing Stackelberg equilibria and optimizing context length in multi-agent reinforcement learning. These advancements have the potential to impact various applications, including economics, finance, and autonomous systems. Noteworthy papers include: Solving Continuous Mean Field Games: Deep Reinforcement Learning for Non-Stationary Dynamics, which introduces a novel deep reinforcement learning algorithm for non-stationary continuous mean field games. Adaptive Context Length Optimization with Low-Frequency Truncation for Multi-Agent Reinforcement Learning, which proposes a novel framework for adaptive context length optimization in multi-agent reinforcement learning.