We introduce World SLAM Model (WSM), a unified framework that brings the SLAM paradigm directly into downstream navigation. Rather than treating SLAM merely as an upstream module that provides poses, maps or tokens, WSM adopts its core mechanisms, including incremental state updates with persistent memory and backend r...
Ming-Hui Qin, Yi-Jun Yuan, Wei-Cheng Zheng et al.· 0 citations
The proposed Representation World Model, which learns states, transitions, and executable plans directly in representation space, suggests that planning directly in representation space provides a promising alternative to conventional world-model planning.
Yi-Jun Yuan, Wei-Cheng Zheng, Wei-Bang Wang et al.· 0 citations
LoSA is a training-free sparse-attention method that fixes a retained-mass threshold of 99% rather than a sparsity ratio: it measures exact block attention masses at one early dense step, keeps, for each head and query block, the smallest key/value block set meeting the threshold, and reuses the frozen block indices fo...
En-Huai Liu, Yun-Ke Wang, Yu-Tong Wang et al.· 0 citations
TASSO, a new paradigm that efficiently preserves the latent space geometry while ensuring network plasticity, is introduced with two complementary techniques: subspace learning and geometry-aware knowledge distillation.
Chang-Ming Sun, Francesco Barbato, Matteo Caligiuri et al.· 0 citations
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