During July 2026, work focused on extending the jd-opensource/xllm repository to support MUSA-accelerated Qwen3.5 dense models, targeting high-performance inference on MUSA-enabled GPUs. This involved implementing CUDA-based kernels and layers for activation, attention, and gating, as well as integrating FlashInfer-based attention backends and gated delta network (GDN) support. The build system and verification logic were updated using CMake to conditionally enable MUSA optimizations, ensuring scalable deployment. Leveraging expertise in C++, CUDA, and high performance computing, the developer validated integration with existing xLLM pipelines, laying the foundation for broader model support and faster inference in production environments.