General-purpose embodied manipulation hinges on a unified action representation that generalizes across embodiments and scales readily. Yet existing policies rely on embodiment-specific action spaces, making cross-embodiment demonstrations difficult to leverage at scale and limiting transfer to new embodiments and spat...
Song Liu, Lin-Ying Li, Yan-Shun Zhao et al.· 0 citations
REFLEX is proposed, a training-free method that keeps the default router unchanged while reorganizing expert computation around the evolving refinement process, and introduces a coarse-to-fine hierarchy for expert-budget allocation that aligns computation with block-relative refinement roles while using the Frontier-Pr...
Xiang-Wen Xia, Chen Yan, Yiming Zhang et al.· 0 citations
Mixture-of-experts vision-language models (MoE-VLMs) increase model capacity with sparse expert activation, yet deployment requires storing the full expert pool. Training-free expert merging reduces this burden, and many routing-based methods aggregate routing statistics across all tokens to determine merge compatibili...
Hong-Yu Zhang, Cheng Yan, Xiang-Wen Xia et al.· 0 citations
This framework is the first controlled, pre-registered instrument for this choice and never reads catch alone, and recovers up to 0.95 informedness over eight-action review, and no tested label-blind policy consistently beats it.
Yuchen Han, Cheng Yan, Wuyang Zhang· 0 citations
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