Jackie Xu extended the TorchBench benchmarking suite in the pytorch-labs/tritonbench repository by adding support for the tlx_matmul input operation, broadening the framework’s ability to evaluate matmul-related workloads. To improve reproducibility and testability, Jackie refactored the input ingestion process to read tlx_matmul data from files rather than relying on hardcoded or ad hoc sources. This approach aligns with ongoing efforts to standardize data processing within the suite and ensures more accurate benchmarking results. The work was implemented using Python and PyTorch, demonstrating a focused application of machine learning and data processing skills to enhance benchmarking infrastructure depth.