Worked on hardening the Torch-MLIR lowering process in the llvm/torch-mlir repository, focusing on improving correctness and stability for edge cases in tensor operations. Addressed issues in Torch-to-Tosa lowering by implementing robust handling for empty memory formats with zero-sized dimensions, ensuring invalid intermediate representations are avoided. Refactored the transposed convolution lowering logic to correctly support negative and mixed-mode padding, introducing two safe execution paths and unifying tensor slicing operations. Expanded targeted test coverage to validate these changes, reducing the risk of out-of-bounds errors in production models. Utilized C++, MLIR, and Python to deliver these improvements collaboratively.