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RoboDawn is investigated, a human-intuitive interface that exposes robotic control to an agentic VLM through a compact set of discrete translation, rotation, and gripper commands, and an in-context learning (ICL) scheme is introduced that ground the VLM in both interface usage and task-solving strategies.
GUICrafter achieves superior GUI agent performance with just a fraction of the data, revolutionizing the way we think about training in data-scarce environments.
Effective keyframe extraction can boost MLLM performance by over 2% on complex video-guided tasks, revealing a critical link between video understanding and procedural learning.
TopoCap enables seamless motion retargeting across arbitrary skeletal structures, outperforming traditional methods and opening the door to unprecedented animation flexibility.
LLMs can now optimize CUDA kernels across diverse scientific computing and LLM workloads, rivaling hand-tuned libraries like cuBLAS.