The field of multi-agent path finding and task allocation is experiencing significant growth, with a focus on developing efficient and scalable algorithms for dynamic environments. Recent research has explored new approaches to handle conflicts, improve response times, and enhance situational awareness. Notably, advancements in decentralized algorithms have led to improved scalability and solution quality, making them suitable for large-scale applications. Additionally, innovative frameworks have been proposed to tackle complex task allocation problems, reducing penalties and improving delivery performance in time-critical environments. Another key area of research is the development of algorithms for streaming multi-agent pathfinding, which has shown promise in reducing runtime for scenarios with prolonged working hours. Noteworthy papers include: Multi-Agent Path Finding via Finite-Horizon Hierarchical Factorization, which introduces a novel algorithm that achieves up to 60% reduction in time-to-first-action. PRISM: Complete Online Decentralized Multi-Agent Pathfinding with Rapid Information Sharing using Motion Constraints, which demonstrates scalability and solution quality, supporting 3.4 times more agents than centralized approaches.