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Tsinghua University
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The model family shows gains in held-out scientific-code repair and across selected general-purpose benchmarks in code, reasoning, and knowledge, providing evidence of positive transfer from scientific experience to broader capabilities.
Traffic element awareness can dramatically elevate the performance of autonomous driving systems, achieving state-of-the-art results with minimal architectural changes.
Integration of diverse robot policies can be streamlined from hours to minutes, revolutionizing how we deploy and evaluate robotic systems.
Automated synthesis can transform the personalization of animatronic faces, enabling rapid adaptation to diverse facial geometries with minimal manual intervention.
High-diversity training improves safety in VLA models, but sub-optimal trajectory synthesis still hinders task success.
The hardest AI tasks remain largely unsolved, with current models achieving only a 2.6% success rate on economically valuable workflows.
