China’s AI Future Map Exceeds DeepSeek’s Genius Girl

Prelude to China’s AI Automobile-Cell Phone-Robot-House Integrated AI Era Launched by Xiaomi MiMo-V2-Flash

Reversal: Huge waves brought about by talent displacement
Yesterday, Dec. 17, there was a pretty interesting technology announcement: Luo Puli, a genius boy born in 1995, who is the main character of the development of DeepSeek with Liang Wenfeng. She made her first public appearance since joining Xiaomi in November this year, and she is currently the lead developer of the MiMo team, the home-car-mobile-home appliance integration model that Xiaomi focuses on.

Immediately after joining, she presented her vision that “intelligence must eventually move beyond language to the physical world.” She is developing a large model in just one month.

MiMo-V2-Flash is considered “genius girls beat their parents (DeepSeek)” in that it has overpowered existing strongmen armed with huge capital and volume attacks beyond just one researcher’s achievements with Xiaomi’s unique “practical intelligence.”

Technical essence: Efficiency of extreme steel completed with ‘以 small 博’
According to a technical report released by Xiaomi, MiMo-V2-Flash is focused on the efficiency of producing exceptional results with limited resources.

Intelligent MoE Architecture: This model has a total of 309B parameters, but adopts an innovative Mixed-of-Experts (MoE) architecture that activates only 15B in real-world inference. This is an incredible efficiency, with less than half the resources compared to competing models such as DeepSeek-V3.2.
Paradigm shift in inference: Hybrid Attention technology combines sliding windows (SWA) and global attention (GA) at a 5:1 ratio. This reduces the amount of computation and KV cache storage for long contextual processing by a factor of 6.
Revolution of Speed: We introduced Multi-Token Predictive (MTP) technology to Speculative Decoding, increasing the decoding speed by 2.6x. In addition, it ranked as the overwhelming number one among global open-source models, recording 73.4 percent on SWE-Bench Verified, which evaluates software development capabilities.

Embodied AI Integration
Xiaomi’s real ambition lies in “vertical sequencing” of implanting this powerful algorithm into physical reality.

Dualization of cerebral and neural networks: We have built an architecture that links MiMo-V2-Flash (brain), which is dedicated to strategic reasoning, and MiMo-Embodied, which is responsible for real-time physical control.
Seamless Collaboration Scenario: When a user arrives in an electric car SU7, the vehicle’s sensors are aware of the situation and direct the implementation instructions to CyberDog through the cloud to “carry the luggage from the trunk into the house.” The ecosystem, where cars and robotic appliances work together as a single intelligence, is also the direction Tesla and Google are aiming for.

Cloud-Edge Collaboration

1) MiMo-V2-Flash (strategic brain) is responsible for the cloud layer

MiMo-V2-Flash is optimized for cloud infrastructure in terms of scale and performance.

Vast parameters: a large model with a total of 309B (309 billion) parameters, which has a very high memory share to run alone on an on-device.
Advanced inference: Aiming for DeepSeek-V3.2 or GPT-5 (High) level performance, it serves as a “central control room” for complex code generation and agent workflows.
Cost and Speed: When running in the cloud, it delivers an overwhelming cost-effectiveness of $0.1 input and $0.3 output per million token and handles large-scale operations.

2) Edge: MiMo-Embodied and Lightweight Models (Reflective Neural Networks)
Inside the robot (CyberDog) or automobile (SU7), lightweight models with real-time life are rotated.

Real-time Action: A lightweight model (e.g., MiMo-Embodied 7B) running from the device’s internal NPU (self-chip) is responsible for tasks that require a quick response of less than 10 ms, such as the robot avoiding or balancing obstacles.
Distillation: Multi-Teacher On-Policy Distillation (MOPD) technology mentioned in the report transfers the knowledge of the cloud’s giant model (teacher) to the lightweight model (student) for the edge to create a “small but smart” neural network.

3) The way the two models collaborate
They move as one through a “hierarchical decision-making” structure.
(1) Receive user commands: When the user says “go to the kitchen and get a red cup,” this voice data is sent to V2-Flash in the cloud.
(2) Formulating a strategy (cloud): V2-Flash analyzes the situation and makes a full plan of “Move the kitchen -> identify the cup -> Grasping -> return”.
(3) Deliver action instructions: the plan is immediately sent to the robot’s edge model.
(4) Real Motion (Edge): The MiMo-Embodied model inside the robot accurately calculates the position of the cup with the camera and moves the arm joints to complete the physical action according to the instructions of the V2-Flash.

Acceleration technology: MTP (Hybrid Acceleration)
The report explains that Multi-Token Prediction (MTP) technology speeds up inference by a factor of 2.6. The technology is responsible for predicting the next behavior when making a decision in the cloud and sending it to the edge device, reducing communication latency beyond the real world.

In conclusion, Xiaomi is overcoming hardware constraints with its software architecture by designing “thoughts are deep and wide in the cloud (V2-Flash) and actions are fast and accurate at the edge (Embodied).