Worked on stabilizing distributed broadcasting in tensor-parallel configurations for the flashinfer repository, focusing on reliability and scalability in multi-GPU inference. Addressed a critical bug in TorchDistBackend.bcast by ensuring the sender rank was correctly interpreted within distributed sub-groups, which prevented runtime crashes in NCCL-based tensor-parallel setups. Enhanced the robustness of CUDA graph capture by adding targeted distributed tests that validated allreduce fusion workspace creation across various GPU and subgroup configurations. Leveraged Python programming, distributed computing, and unit testing to reinforce code quality, maintaining comprehensive test coverage and integrating pre-commit checks to support ongoing code health and continuous integration standards.