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
Denoising-Aware Visual Evidence Trajectory Allocation (DAVET), a training-free framework that allocates visual evidence according to the evolving generation state, achieves an average speedup of 1.55 times with an average relative performance drop of 1.86%, showing that denoising-aware visual evidence allocation can re...
Yongkang Zhou, Xiang-Wen Xia, Cheng Yan 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
RoleMerge, a training-free method that constructs each expert's Routing Role Profile (RRP) from phase-normalized routing statistics, capturing its relative phase preference, is proposed, and results validate phase-conditioned expert roles as a more effective basis than global routing aggregation for MoE-VLM expert merg...
Hong-Yu Zhang, Cheng Yan, Xiang-Wen Xia et al.· 1 citation
This work presents $\Phi$-Bench, a benchmark for systematically evaluating LLMs on engineering the LLM infrastructure stack and spans tasks of varying complexity, ranging from localized kernel-level function completion to long-horizon implementation and end-to-end system optimization.
Lei-Lei Ding, Shu-Min Wang, Yu-Ting Huang et al.· 1 citation
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.
