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LEEVLA reveals that effectively guiding attention to task-critical evidence can dramatically enhance performance in vision-language-action tasks.
Binarization methods that ignore weight significance can lead to substantial performance losses, but SAB-LVLM optimizes this process, achieving superior efficiency without sacrificing accuracy.
SC3-Eval achieves a remarkable 0.929 Pearson correlation in evaluating robot policies, revealing critical insights into their real-world performance.
Ling-2.6 and Ring-2.6 achieve unprecedented efficiency in agentic intelligence, enabling instant responses and deep reasoning at trillion-parameter scale.
Ditch the clunky architectures: a single diffusion model can now handle vision, language, and robot control to achieve SOTA manipulation performance.
