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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.
LLMs can sift through routine clinical notes to detect epilepsy with high accuracy, even boosting expert neurologists' diagnostic performance by over 10%.
Biomedical language models suffer severe catastrophic forgetting when sequentially updated, but parameter isolation offers the best retention per GPU-hour, revealing a crucial efficiency-stability tradeoff.
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.
