World and action modeling are closely related, yet their reciprocal benefits under unified causal training remain unclear. We introduce OneWM, a simple framework for unified causal training that treats actions as both conditions for world prediction and targets for policy learning within a shared video-action architect...
Yue Su, Bo-Kai Lin, Xin-Kai Wang et al.· 1 citation· ⚡1
While content-adaptive schemes have delivered notable advances in image super-resolution (SR), existing approaches typically focus on texture complexity and ignore intrinsic degradation factors (e.g., blur kernels or noise patterns), leading to suboptimal computation allocation and reconstruction performance. To remedy...
Song-Li Wu, Hao-Nan Jiang, Ji-Xuan Fan et al.· 0 citations
This work proposes an agent-based active exploration framework that enables robots to dynamically interact with the environment rather than merely execute predefined instructions, and introduces a fine-grained perception-execution interleaving strategy, which tightly couples visual feedback with skill execution to impr...
Shi-Lin Ma, Chu-Bin Zhang, Xu-Long Bai et al.· 0 citations
Skill-Based Memory (SkillMemo) framework is proposed that implicitly decomposes long-horizon demonstrations into latent atomic skills and integrates skill-level features into a dynamic episodic memory bank for solving compositional tasks and consistently enhances both DP and VLA backbones.
Changyuan Wang, Chu-Bin Zhang, Zhen-Yu Wu et al.· 0 citations
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