Worked on the ml-explore/mlx-lm repository to improve the reliability of batch caching components used in machine learning inference workloads. Addressed a batch dimension mismatch issue in BatchKVCache and BatchRotatingKVCache by implementing dynamic batch size calculation based on cache offset, which prevents runtime errors when extending caches with empty or non-empty batches. Enhanced the robustness of these data structures by adding comprehensive unit tests that validate edge cases, ensuring safer scaling in production environments. Utilized Python and applied expertise in data structures and unit testing to deliver more stable batch processing, reducing the likelihood of production incidents related to caching.