MiMo-V2.6 just dropped, and Xiaomi’s Fuli Luo is already teasing the next architecture: MiMo-V3🔥 The core of it, HySparse2, targets three bottlenecks in agentic inference: prefill cost, KV cache size, and long-context retrieval. Compared with MiMo-V2.6’s Hybrid SWA architecture at 1M tokens: • 5.02× lower prefill FLOPs • 4.5× smaller KV Cache • Better MRCRv2 and RULER-v2 scores, plus lower AgentPPL and LongPPL The HySparse2 paper comes from Xiaomi’s LLM-Core team, with Fuli Luo as corresponding author and Team Lead. Interestingly, its references also include DeepSeek-V2, V3.2, V4, and V4.1-Flash, along with OpenAI’s GPT-4.1 evaluation work and the gpt-oss model card.