The field of multi-agent systems is witnessing significant developments, with a growing emphasis on cooperative game theory and reinforcement learning. Recent research has focused on addressing the challenges of scalability, computational efficiency, and personalized adaptation in multi-agent systems. A key direction is the integration of game-theoretic optimization with systematic hybrid system design, enabling rapid emergency response capabilities and exponential convergence to consensus. Another important area of research is the development of parameter-efficient collaboration frameworks that balance global coordination with local adaptation. The introduction of new benchmarks, such as those tailored for continual multi-agent reinforcement learning, is also facilitating progress in this field. Notable papers include:

• Shapley Machine, which models and solves n-agent ad hoc teamwork through cooperative game theory and reinforcement learning.
• A Hybrid Adaptive Nash Equilibrium Solver, which integrates distributed game-theoretic optimization with systematic hybrid system design.
• PE-MA, which proposes a novel collaboration framework for efficient and personalized co-evolution in multi-agent systems.
• MEAL, which introduces a benchmark for continual multi-agent reinforcement learning, enabling the development and analysis of algorithms in this domain.