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A new architecture exploration for Xiaomi MiMo-V3. HySparse2 explores a new architecture designed for long-context, multi-turn agent tasks — reducing Prefill computation, lowering KV Cache usage, and improving long-context retrieval. Discover the research behind Xiaomi MiMo-V3 ↓ #XiaomiMiMo #XiaomiAI #AIAgent
MiMo-V3 is getting a new architecture. The core of it, HySparse2, is out today. Less prefill, a smaller KV cache, better long-context retrieval—and we got all three at once. Compared with MiMo-V2.6's Hybrid SWA architecture: • 5.02× lower prefill FLOPs at 1M tokens • 4.5× smaller KV cache at 1M tokens • Better MRCRv2 and RULER-v2 scores, plus lower AgentPPL and LongPPL Why build a new architecture? Agentic inference is a very different workload. Each round, a short action can return a long observation that needs to be prefilled, while the context keeps growing. That puts prefill cost, KV-cache size, and retrieval accuracy on the critical path at the same time. HySparse2 tackles all three with two levels of KV sharing: • KV Bridging: Following YOCO, full-attention layers in the cross-decoder build their K/V from self-decoder hidden states. • KV Reuse: Within each hybrid block, sparse layers reuse the preceding full-attention layer's KV cache and selection indices. Two more changes: token-level selection replaces block-level selection, and a forced window of recent tokens replaces the separate SWA branch, so local and global tokens share one KV cache. Since all cross-decoder KV caches now come from the self-decoder, prefill can stop once the self-decoder finishes. Paper: arxiv.org/pdf/2609.26368
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Today, we’re celebrating bold ideas. 🎉 The 7th Xiaomi Hackathon Awards Ceremony has come to a close! We welcome new ideas, new experiments, and the possibilities that emerge when technology meets creativity. Congratulations to all the winning teams, and thank you to everyone who took part! Here’s to turning more ideas into reality, together. #XiaomiHackathon #XiaomiAI
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Another example of Xiaomi MiMo in creative work. Xiaomi MiMo-V2.6-Pro generated an orchestral piece featuring around ten instruments based on creative requirements, then autonomously converted the score into MIDI. The process demonstrates its understanding of different instrumental roles and orchestration, as well as its ability to apply musical knowledge to melody creation and overall arrangement. #XiaomiMiMo #XiaomiMiMoV26 #AIGeneration #XiaomiAI
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Here’s a more visual example. Xiaomi MiMo-V2.6 can turn text descriptions or reference images into 3D models and scenes in Blender, creating 3D assets for animation, 3D printing, and game development. #XiaomiMiMo #XiaomiMiMoV26 #AIGeneration #XiaomiAI
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A classic mathematical theorem. 6,000+ lines of Lean code. Verified by the kernel. Xiaomi MiMo 2.6 Pro assisted researchers in fully formalizing the original main theorem of Li–Yorke’s classic paper Period Three Implies Chaos in Lean 4. Guided by a research-designed exploration strategy, multiple Subagents collaborated on theorem formulation and proof formalization. After revision and integration, the project spans 6,000+ lines of Lean code, verified by Lean’s kernel with no unfinished proof placeholders. Notably, Xiaomi MiMo 2.6 Pro was not specifically post-trained for Lean. #XiaomiMiMo #XiaomiMiMoV26 #AIforScience #XiaomiAI
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From a research question to candidate materials. Xiaomi MiMo-V2.6-Pro worked with Xiaomi’s materials researchers to explore new MOF designs for capturing PFAS — reviewing literature and patents, proposing and checking design hypotheses, and running computational “dry-lab” experiments to identify promising candidates for further validation. The team estimates a 10× productivity gain, shortening the R&D cycle from one month to 2–3 days. As Prof. Jinhu Dou of Peking University put it: “In my view, its work on this project—from literature review to materials design and computational evaluation—was on par with that of a well-trained doctoral researcher.” Read the full case study: mimo.xiaomi.com/blog/mimo-v2… #XiaomiMiMo #XiaomiMiMoV26 #AIforScience #XiaomiAI
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Xiaomi MiMo-V2.6-Pro ranks as the top open-weight model on the Artificial Analysis Intelligence Index, with a score of 46.32.
MiMo-V2.6-Pro debuts as the top open weights model on the Artificial Analysis Intelligence Index (46). At $0.13 per Intelligence Index task, it lands on the Intelligence vs. Cost per Task Pareto frontier @Xiaomi has just released MiMo-V2.6-Pro, an open weights model with major advances in intelligence over its predecessor, MiMo-V2.5-Pro (Intelligence Index: 26). Despite the improvement, it retains the same attractive pricing at $0.435 per 1M input tokens (with a 99% cache-hit discount) and $0.87 per 1M output tokens. This makes MiMo-V2.6-Pro one of the most cost-efficient models to deploy. MiMo-V2.6-Pro is an MoE model with 1.02T total parameters and 42B active parameters. Stay tuned for additional analysis of the model. Check out MiMo-V2.6-Pro full benchmarking breakdown here: artificialanalysis.ai
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A major new release from Xiaomi MiMo. Xiaomi MiMo-V2.6 is now officially released, with Pro and Flash models now open sourced alongside the technical report, 7K+ RL task environments, an end-to-end RL framework, and composable mini-harnesses. Built through scaled Reinforcement Learning, MiMo-V2.6 pushes further across complex Agent tasks, coding, computer use, 3D reasoning, scientific research, and creative work. Xiaomi MiMo Desktop and membership plans are also launching alongside Pro and Flash, with MiMo-V2.6-Pro UltraSpeed now available in Desktop and through the API, delivering up to 20× faster generation. #XiaomiMiMo #XiaomiMiMoV26 #OpenSource #XiaomiAI
Introducing Xiaomi MiMo-V2.6 — Pro & Flash. Frontier intelligence, all the modalities, built in public. 🔹 Two omnimodal models, advancing through scaled reinforcement learning 🔹 Pro performs on par with Claude Opus 5 and GPT-5.6 Sol across most agent benchmarks 🔹 Pro scores 46 on the Artificial Analysis Intelligence Index — the highest among open-source models 🔹 Stronger coding, computer use, 3D reasoning and creative capabilities 🔹 Open model weights, technical report, RL environments and training code Blog:mimo.xiaomi.com/mimo-v2-6
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What if AI could hear only the person you want to listen to? Xiaomi has officially released and open-sourced Xiaomi-CocktailASR-1, an industrial-grade target-speaker ASR large model designed to tackle the “cocktail party” problem. With a reference speech prompt, Xiaomi-CocktailASR-1 can identify and transcribe the target speaker in complex multi-talker environments. It achieves SOTA performance across multiple mainstream multi-speaker benchmarks, while also delivering single-speaker recognition performance comparable to standard ASR models. The model also rejects non-target speakers, outputs empty text when the target speaker is absent, and supports Chain-of-Thought reasoning for interpretable recognition results. 🔗 github.com/xiaomi-research/x… Let AI “hear” only your voice. 🎙️ #XiaomiAI #ASR #SpeechAI #OpenSource
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Xiaomi-Robotics-U0 is now fully open source. 🤖 We’re releasing the Xiaomi-Robotics-U0 World Model training and inference framework, and model weights in 4B and 34B, supporting: • Embodied scene generation & editing • Single-image generation & image editing • Video generation • Interleaved vision-language sequence generation We hope this open release lowers the barrier to experimentation and accelerates the exploration and real-world application of world models in robotics. Website:robotics.xiaomi.com/xiaomi-r… GitHub:github.com/XiaomiRobotics/Xi… Hugging Face:huggingface.co/collections/X… #Robotics #EmbodiedAI #WorldModel #XiaomiAI #XiaomiRobotics
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You bring the idea. Xiaomi MiMo Desktop takes care of the rest. From complex workflows to polished, finished results, MiMo Desktop can turn your ideas into work that’s ready to use — handling the process from start to finish, and making sure the result is done well. Now in invite-only beta. We can’t wait to see what you create with MiMo. 🚀 #MiMoDesktop #AIAgent #XiaomiAI
🚀 Xiaomi MiMo Desktop is now in invite-only beta. Invitees also get free, limited access to next-gen MiMo models during the beta. MiMo Desktop is a desktop agent — not another chat window. It takes your raw materials — Office files, images, video, audio, even zips — understands the goal, plans the path, and ships finished, editable output. What it can do: 🔹 Previews you can touch. Games, dashboards, demos load live in-session — click, break, and keep iterating. No local frontend setup required. 🔹 Edit by selection. Click or box-select any region — describe the change, and only that part updates. Every revision is versioned, so you can roll back anytime. 🔹 Smart dispatch. The system routes tasks to the right model and agents for you — quality when it matters, speed when it doesn’t. No model-menu guesswork. 🔹 Browser & computer use. It drives your browser to research, fill forms, and extract assets, then feeds findings straight into the task. Full computer use — screen, keyboard, mouse, cross-app work — with record & replay for repeatable flows. 🔹 Long tasks stay affordable. Partial regeneration + cache hit rates up to 99% in-session. Seats are limited. Apply here: mimo-ai.xiaomimimo.com/deskt…
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🚀 Can tabular data scale like language? Meet Xiaomi-TabLDM, our new tabular foundation model combining large-scale synthetic pretraining, efficient capacity scaling, and test-time scaling. With a unified architecture and default settings, it supports classification and regression across datasets, with no dataset-specific training or fine-tuning. Results 👇 🏆 Strong regression performance across 4 benchmarks: Tied for #1 on OpenML-CTR23; and 2nd on regression across TALENT, TabArena, and BCCO. ⚡ Favorable performance-efficiency trade-off: Achieve the #2 Elo on TabArena while using 82\% less training time and 68\% less prediction time than the top-ranked TabFM. The code, model weights, and technical report are now available. 🔗 github.com/xiaomi-research/x… 🔗 huggingface.co/occams/Xiaomi… 🔗 arxiv.org/abs/2609.03880
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🏆 1st place + 2 awards at IJCAI-ECAI 2026. The Darwin Agent team, jointly developed by Xiaomi, Nanjing University, and Tsinghua University, won the Open Track of the official CAR-bench Challenge, taking home both the Rank Award and Innovation Award. Powered by TRACE, the team achieved a 70% stable pass rate — 20 percentage points above the official baseline. The goal: make agents not just capable of completing tasks, but consistently reliable when facing ambiguity, missing capabilities, and real-world constraints. #AI #Agent #AIAgent #IJCAI2026 #XiaomiAI
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Meet Three Xiaomi XRING Chipsets At IFA. Xiaomi just dropped THREE self-developed chipsets——Xiaomi XRING O3. O100. D100. It's a chip revolution. Full story at IFA Berlin. #Xiaomi #XiaomiXRING #IFA2026 #TechSecret
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AI is moving beyond the screen and into real life. At IFA 2026, we’re showing how AI can understand, connect, and proactively serve people across devices, cars, and homes. Come visit us and experience AI in action. 👋 📍 Hall 6.2, Booth 101
Xiaomi makes its IFA debut with our largest-ever "Human × Car × Home" smart ecosystem showcase. A diverse portfolio spanning every aspect of life, seamlessly connected over 1.16 billion devices worldwide. More than 380 products are showcased at IFA, while bringing AI beyond the screen and into the physical world. And Xiaomi CyberOne is here. Hall 6.2, Booth 101. Come find us. 🤖
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🏆 8 Xiaomi papers accepted at EMNLP 2026! The papers span mobile agents, efficient LLM training and inference, AI-assisted model development, and proactive intelligence. 👇 #EMNLP2026 #NLP #XiaomiAI
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🧠 AI R&D and Proactive Intelligence: 🔹 ECO-SCE — screens architecture edits before costly model training 🔹 PRPF — perceives first, reasons only when it should step in
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From mobile-agent RL powered by self-evolving tasks to models that know when to think deeper, these 8 papers show how smarter choices at every stage make AI more efficient, reliable, and ready for real-world use.
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