During March 2026, Kol22 focused on stabilizing continuous batching for Qwen models in the Blaizzy/mlx-vlm repository. They addressed a critical issue where mismatches in batch dimensions and cache states could disrupt attention computation, particularly in streaming and incremental inference scenarios. By implementing robust error handling and introducing guards against discrepancies in sequence lengths, Kol22 improved the reliability of the attention mechanism under continuous batching. Their work, primarily using Python and leveraging skills in attention mechanisms and model optimization, demonstrated a deep understanding of edge-case failures and contributed to more stable and predictable model behavior in production workflows.