Worked on improving reliability and correctness in large-scale machine learning systems, focusing on backend development and infrastructure. Addressed critical bugs in the pytorch/pytorch and jeejeelee/vllm repositories by fixing dtype inference in weighted bincount operations, ensuring output types matched weights to prevent downstream tensor errors. Enhanced the initialization process for EagleMistralLarge3Model by adding missing attributes and logic for weight loading and runtime execution, as well as configuring expert redundancy. Introduced a validation test suite in vLLM to verify model initialization and behavior, using Python and PyTorch to strengthen regression coverage and support safer deployment of compiler and inference features.