Kunlun Unveils World Model Matrix
机器之心 · wechat · 2026-07-19
This WAIC feature focuses on Kunlun's "first year of world models," highlighting three main tracks: World Model Foundation, Embodied AI, and AI Music.
For embodied AI, the article introduces Riemann-1.0, defined as a universal robotic brain/WorldActionModel. It unifies action generation, environmental state evolution, and future visual prediction into a causal generative framework. Trained on human egocentric video, exoskeleton/glove data, and robotic trajectories totaling 232,000 hours, the reported benchmark results include LIBERO 99.0%, RoboCasa-365 62.6%, and RoboTwin2.0 94.3%. Adding human egocentric pre-training reportedly boosts long-horizon task success rates significantly.
Regarding the world model foundation, the spotlight is on Matrix-Game3.5. Designed for long-term consistency, real-time interaction, and open-world generation, it introduces mechanisms like PatchMemory, PRoPE, and decoupled dynamic-static memory. Despite having only 5B parameters, the model can achieve real-time streaming generation at 720P and roughly 20FPS on a single GPU, and it has been open-sourced. This world model is currently applied in gaming, digital twins, simulations, and embodied AI training environments.
In content generation, the article details two music models, MurekaV9.5 and O3, emphasizing their "restrained arrangement" and "reflection capabilities" to better align with human aesthetics. The article provides several standardized evaluation metrics, such as a 61.0% good vocal rate, a 97.0% good prompt control rate, and a 95.7% full genre representation rate.
Related event: Kunlun Tech Open-Sources Matrix-Game 3.5 Interactive World Model(3 posts)→
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