During February 2026, Michael Cotton focused on enhancing test reliability for loss functions in the tenstorrent/tt-forge-fe repository. He refactored the NLLLoss test suite to use a verify-based validation approach, introducing an NLLLossWrapper that bridges Forge’s one-hot encoded targets with PyTorch’s class index requirements. This adjustment standardized the testing process, reduced flakiness, and improved cross-version compatibility. Working primarily in Python and leveraging PyTorch, Michael’s changes addressed a key bug by aligning test expectations and implementation. The work demonstrated depth in software testing, machine learning integration, and test framework design, resulting in more robust and maintainable regression detection workflows.