June 2025 focused on expanding TritonBench benchmarking capabilities, improving profiling fidelity, and strengthening stability and code quality. Delivered a new exponential kernel path and benchmarking support for TritonBench, enabling direct comparison of Triton exp against PyTorch exp through the vector_exp kernel. Expanded multi-precision benchmarking for vector_exp across FP32/FP16/BF16 with half-precision profiling to revealTorch vs Triton performance across dtypes. Implemented GPU timing instrumentation across CUDA and AMD, adding a dedicated timing kernel and AMD timing for vector_exp to deepen performance analysis. Added jagged_sum profiling and occupancy metrics to quantify kernel efficiency, informing optimization opportunities. Improved reliability and maintainability with plotting stability fixes (eliminating FileNotFoundError), API compatibility adjustments (constexpr instantiation for Triton), and lint fixes to keep the codebase clean. Business value: these changes enable more accurate, hardware-aware performance insights, faster optimization cycles, and reduced downtime in benchmarking dashboards, directly supporting data-driven hardware and kernel tuning decisions for ML workloads.