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A unified multimodal agent, OneSearch-VL, achieves state-of-the-art performance on diverse visual research tasks by explicitly modeling and preserving interdependencies within a Visually Grounded Evidence Graph.
TRACE achieves over 50% accuracy in long-video question answering by ensuring every answer is grounded in comprehensive visual evidence, outperforming traditional methods at a fraction of the frame cost.
A single distilled model can outperform larger heterogeneous teachers by effectively integrating their strengths without interference.
CR-Solver achieves over 95% success rates and millimeter-level accuracy in motion generation for continuum robots, all while leveraging GPU acceleration for unprecedented speed.
