arXiv 2610.10528 · cs.RO · 2026-10-07
Long-WAM: Scaling the Context of World-Action Models
Wei Huang, Bohan Zhang, Chenzhi Liu, Isabella Liu, Shuai Yang, Weian Mao, Luozhou Wang, Yicheng Xiao 等 16 人
NVIDIA
在最新榜 #12 本站收录于 2026-10-21
一句话要点
Long-WAM 研究在实时控制约束下给世界-动作模型加长上下文，自回归预训练的视频底座收益最大。
Long-WAM scales the context of world-action models under real-time limits and finds autoregressive video pretraining makes long history pay off.
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数据说明
元数据来自 arXiv API（已核实）；点赞来自 Hugging Face 每日论文；引用来自 Semantic Scholar。最近刷新 2026-10-22。
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