Worked on improving numerical stability for complex-number arithmetic in PyTorch, specifically addressing inconsistencies between GPU and CPU calculations. Focused on the pytorch/pytorch repository, the developer replaced the unstable GPU implementation of complex exponentiation with a direct multiplication approach, ensuring that squaring complex numbers on CUDA devices now matches CPU results. This targeted fix reduced numerical instability and debugging overhead for users working with complex-valued models. The work involved deep understanding of GPU programming, numerical methods, and unit testing, and included thorough validation to ensure cross-device consistency, ultimately enhancing the reliability and maintainability of PyTorch’s core mathematical routines.