In September 2025, Jiayi Liu enhanced reliability and benchmarking for machine learning workloads in the pytorch-labs/helion and pytorch-labs/tritonbench repositories. Jiayi focused on improving safety in eager execution by introducing error handling to prevent unintended code printing, and expanded benchmarking capabilities by adding support for scalar bias in addmm operations. Using Python and PyTorch, Jiayi also addressed accuracy issues in operator comparisons by ensuring correct weight copying for swiglu and liger layers. The work demonstrated depth in accuracy testing, model optimization, and error handling, resulting in more trustworthy performance signals and fairer operator evaluations for deep learning research.