The field of High-Performance Computing (HPC) is shifting towards heterogeneous GPU-accelerated architectures, creating a need for modernizing legacy codes to achieve performance portability. Researchers are exploring innovative solutions, including the use of large language models (LLMs) and autonomous agentic AI workflows, to translate and optimize parallel code for diverse hardware. These approaches have shown promise in generating performance-portable codes that surpass traditional baselines. Furthermore, new benchmarks and evaluation frameworks are being developed to rigorously assess kernel performance and correctness. Notable papers include: Towards High-Performance and Portable Molecular Docking on CPUs through Vectorization, which evaluates compiler auto-vectorization and explicit vectorization to achieve performance portability. From Legacy Fortran to Portable Kokkos: An Autonomous Agentic AI Workflow presents an agentic AI workflow that collaborates to translate, validate, and optimize Fortran kernels into portable Kokkos C++ programs. Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization introduces a new benchmark for evaluating kernel performance and correctness, and presents a comprehensive agentic framework for automating CUDA kernel discovery, verification, and optimization.