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A residual memory mechanism allows LLMs to retain crucial context for long-horizon tasks, achieving near full-context performance with only 5.2% of input positions.
The data pyramid framework reveals how the interplay of diverse data sources can unlock new capabilities in embodied agents, highlighting critical gaps in current methodologies.
VLA models struggle not with *what* they see, but with *how* they see it: FocusVLA reveals that directing attention to task-relevant visual regions unlocks significant performance gains in robotic manipulation.
