The field of edge computing and networking is rapidly evolving, with a focus on developing innovative solutions to address the challenges of latency, resource constraints, and dynamic workloads. Recent research has explored the use of reinforcement learning, distributed optimization, and cooperative multi-agent systems to improve the efficiency and effectiveness of edge computing and networking. Notably, the development of modular frameworks, such as those for federated learning and pipeline parallelism, has enabled more flexible and scalable solutions. Furthermore, the integration of techniques like multi-head attention and coordinated multipoint broadcasting has enhanced the performance of edge computing and networking systems. Overall, the field is moving towards more autonomous, adaptive, and cooperative systems that can efficiently support diverse applications and services. Noteworthy papers include: 5GC-Bench, which presents a modular framework for stress-testing and benchmarking 5G Core VNFs, providing actionable insights for capacity planning and performance optimization. CollaPipe, which introduces a hybrid distributed learning framework that integrates collaborative pipeline parallelism with federated aggregation to support self-evolving intelligent networks, improving computation efficiency and reducing end-to-end latency. SPARQ, which develops an iterative approximation algorithm for joint optimization of service placement, routing, and resource allocation under nonlinear delay constraints, substantially improving resource efficiency and the overall cost-delay tradeoff.