During September 2025, Kernelpool contributed to the ml-explore/mlx-lm repository by enhancing model serving reliability and data integrity. They developed a Nested Cache Batching mechanism using Python and PyTorch, enabling cache structures to extend nested caches with corresponding elements, which improved batching performance and data consistency. Additionally, Kernelpool addressed a bug in LongCat Flash MoE by refining the expert weight masking logic, ensuring zero-computation experts were handled correctly and resulting in more accurate and stable inference. Their work demonstrated a strong grasp of backend development and data caching, with careful validation and code review ensuring robust, regression-free deployment.