The field of swarm robotics is moving towards the development of more efficient and adaptive collective behavior in crowded environments. Researchers are exploring the role of noise and chaos in enabling goal attainment and improving the flow of agents in complex systems. New algorithms and control strategies are being proposed to optimize the behavior of swarms, including the use of energy-stable methods and backstepping control barrier functions. Noteworthy papers include: Noise-Enabled Goal Attainment in Crowded Collectives, which demonstrates how noisy motion can disrupt traffic jams and enable flow in crowded environments. Energy-Stable Swarm-Based Inertial Algorithms for Optimization proposes a novel approach to swarm-based optimization that improves the likelihood of identifying the global minimum. Emergent Heterogeneous Swarm Control Through Hebbian Learning introduces a biologically inspired method for swarm robotics that enables the automatic emergence of heterogeneity, resulting in improved swarm capabilities. LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control presents a multi-robot control paradigm that combines centralized planning with decentralized control, enabling scalable and efficient motion synthesis. Modeling Feasible Locomotion of Nanobots for Cancer Detection and Treatment presents a general model for the collective behavior of nanobots in a colloidal environment, with applications in cancer detection and treatment.