Lixiang worked on the deepseek-ai/FlashMLA repository, delivering a targeted feature upgrade to the DeviceAllocation memory management system. By refactoring the backward pass to use PyTorch tensor allocation instead of manual CUDA memory management, Lixiang reduced reliance on cudaMalloc and cudaFree, aligning the codebase more closely with PyTorch semantics. This approach improved compatibility and safety while simplifying future maintenance. The work, implemented in C++ with CUDA and PyTorch, addressed potential fragility in memory handling and laid the foundation for broader PyTorch integration. Over the month, Lixiang focused on engineering depth, prioritizing maintainability and forward compatibility in the project.