The field of distributed algorithms for mobile agents and multi-agent path finding is experiencing significant growth, with a focus on developing innovative solutions for complex problems. Researchers are exploring new approaches to simulate chirality, enabling agents to coordinate without a common sense of direction. Asynchronous collective tree exploration is also being investigated, with the development of distributed algorithms that can efficiently explore unknown trees. Furthermore, improvements are being made to the wake-up time for the Euclidean Freeze-Tag problem, with new strategies achieving better wake-up ratios. Additionally, budget allocation policies for real-time multi-agent path finding are being explored, and new mechanisms for flex distribution in bounded suboptimal multi-agent path finding are being proposed. Noteworthy papers include: Simulating Chirality: Solving Distance-k-Dispersion on an 1-Interval Connected Ring, which presents a novel method for simulating chirality and resolves an open question in the field. Asynchronous Collective Tree Exploration: a Distributed Algorithm, and a new Lower Bound, which introduces a distributed asynchronous algorithm for collective tree exploration and establishes a new lower bound on the competitive ratio. Improved Wake-Up Time For Euclidean Freeze-Tag Problem, which improves the wake-up ratio for the Euclidean Freeze-Tag problem in various settings. Budget Allocation Policies for Real-Time Multi-Agent Path Finding, which explores different policies for allocating the planning budget in real-time multi-agent path finding. New Mechanisms in Flex Distribution for Bounded Suboptimal Multi-Agent Path Finding, which proposes new flex distribution mechanisms to improve the efficiency of bounded-suboptimal multi-agent path finding.