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RynnBrain 1.1 not only outperforms all competitors in embodied cognition tasks but also redefines how robots can be trained for complex manipulation through innovative 3D grounding techniques.
A single generalist model outperforms specialized systems, achieving over 35% improvement in real-world robotic task success.
Visual Evidence Pre-Alignment boosts MLLM performance by ensuring models effectively utilize fine-grained visual evidence, rather than just relying on coarse captions.
Today's visual generation models are often evaluated on the wrong things, leading to inflated performance claims that mask critical failures in spatial reasoning, temporal consistency, and causal understanding.
RynnBrain leapfrogs existing embodied foundation models, offering a unified, open-source spatiotemporal model that excels at physically grounded reasoning and planning across a wide range of benchmarks.
