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FORT-Searcher achieves superior performance by synthesizing training tasks that actively resist shortcut exploitation, transforming how we train deep search agents.
Attention-guided denoising can dramatically enhance reasoning performance in diffusion language models, outperforming traditional post-training methods.
Achieving zero-shot generalization in robotic grasping across diverse gripper designs could revolutionize how robots interact with their environments.
SeeGroup achieves a remarkable 14.75% increase in depth estimation accuracy for transparent surfaces by allowing adaptive layer grouping rather than relying on fixed strategies.
Forget painstakingly curating MVS datasets - simple procedural generation can match or beat real-world data, even at half the scale.
