December 2024 monthly summary for pytorch-labs/tritonbench: Delivered a key feature to enhance benchmark flexibility and realism by refactoring RaggedHSTUAttn to accept separate query, key, and value tensors, and by updating default parameters and input generation to better reflect common usage patterns in generative recommender systems. Aligned default configurations with typical benchmarks to improve evaluation representativeness and cross-run comparability. Overall impact: expanded applicability of RaggedHSTUAttn, more accurate performance signals, and safer evolution of the benchmark suite. No major bugs fixed this month; the focus was on capability extension with risk-conscious changes and stability maintenance. Technologies/skills demonstrated: PyTorch tensor manipulation, interface refactoring, benchmark-oriented test design, and careful defaults tuning to align with industry practices. Business value: improved benchmarking realism and flexibility enable better decision-making, faster iteration, and more trustworthy performance guarantees for downstream systems.