The MiMo-V2.5 series introduces a comprehensive end-to-end inference optimization strategy designed to maximize Hybrid Sliding Window Attention efficiency. By implementing advanced techniques like tiered caching, SWA-aware prefix trees, and optimized processing pipelines, this engineering update significantly streamlines memory management and scheduling to deliver faster multimodal model inference.