Elp Dumont enhanced the pytorch/executorch repository by implementing robust INT16 handling for Arm backend convolution operations. Focusing on quantization, Elp validated quantization parameters directly rather than relying on runtime data types, which improved the accuracy and reliability of quantized convolution paths. Using Python and leveraging expertise in PyTorch and back end development, Elp introduced comprehensive tests to cover diverse scenarios, ensuring the new approach prevented regressions and maintained alignment with PyTorch quantization semantics. This work raised the stability of model inference on Arm devices, demonstrating thoughtful engineering depth in both the technical solution and the thoroughness of validation.