While recent video generation models synthesize highly realistic visuals, they lack a genuine understanding of intrinsic real-world logic. Existing methods attempt to understand the world by internalizing diverse world knowledge, yet constrained by computational overhead or dimensionality alignment, their learning proc...
Bo Tan, Xiang-Dong Zhang, Yan Xia et al.· 0 citations
Large Multi-modality Models (LMMs) have made significant progress in visual understanding and generation, but still face challenges in visual editing, particularly in following complex instructions, preserving appearance consistency, and supporting flexible input formats. To study this gap, we introduce RISEBench, the...
Xue Yang, Pei-Yuan Zhang, Yilun Zhu et al.· 0 citations
V-ICAL is introduced, a novel benchmark designed to evaluate video-based ICL for multimodal agents that seamlessly connects in-context knowledge induction with core agentic capabilities, including state grounding, temporal memory, planning, and adaptation in dynamic environments.
Zi-Qian Fan, Shi-Bo Xu, Jun-Jie Li et al.· 0 citations
TRWORLDBENCH is introduced, a benchmark for evaluating embodied world models through synchronized head, left-wrist, and right-wrist videos and uses 19 metrics to assess tri-view consistency, task alignment, physical and 3D coherence, motion quality, temporal consistency, and visual quality.
Xuan-Yi Liu, Hao-Feng Wang, Rui-Qi Li et al.· 0 citations
Bench2Dex is meant as a platform for studying visuo-tactile learning across dexterous hands and offers a shared setting for algorithm development while tactile hardware and simulation models are still evolving.
Zhen-Jie Yang, Yi-Deng Zhang, Dong-Jie Zhang et al.· 1 citation
ACE-Brain-0.5 is presented, a unified embodied foundation model that organizes robot intelligence into five coupled functions: spatial perception, decision making, embodied interaction, self-monitoring, and self-improvement, and SSR+, which extends Scaffold-Specialize-Reconcile with a Reactivate stage after task-vector...
Zi-Yang Gong, Hao-Ming Gu, Ze-Hang Luo et al.· arXiv.org· 3 citations
WorldScape Policy 2.0 is introduced, a controllable WAM with reasoning-augmented long short-term memory and fine-grained instruction following and in-context adaptation that demonstrates superior capabilities in long-horizon autonomous planning, fine-grained instruction following and in-context adaptation.
Hai-Sheng Su, Zong-Dai Liu, Xin Jin et al.· arXiv.org· 2 citations
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