The field of distributed systems and game theory is witnessing significant advancements, with a focus on improving scalability, security, and decision-making in complex systems. Researchers are exploring new approaches to consensus protocols, such as hybrid protocols that adapt to different network conditions, and developing more efficient algorithms for solving mean field games and constraint satisfaction problems. Additionally, there is a growing interest in applying game-theoretic models to real-world problems, including deception detection and resource allocation in decentralized markets. Notable papers in this area include: Learning in Stackelberg Mean Field Games: A Non-Asymptotic Analysis, which proposes a novel actor-critic algorithm for solving Stackelberg mean field games. Angelfish: Consensus with Optimal Throughput and Latency Across the Leader-DAG Spectrum, which presents a hybrid consensus protocol that achieves state-of-the-art throughput and latency. chainScale: Secure Functionality-oriented Scalability for Decentralized Resource Markets, which introduces a secure hybrid sidechain-sharding solution for boosting throughput and reducing latency in decentralized resource markets.