Vision-and-Language Navigation (VLN) requires embodied agents to ground language in visual observations and make long-horizon decisions. Existing methods often address these demands through task-specific architectures. As multimodal large language models (MLLMs) become increasingly capable, an important question remain...
Ling-Yi Hong, Wei-Ren Cai, Jia-Yu Chen et al.· 0 citations
Existing VLA pruning strategies primarily select individual visual tokens according to task-level semantic relevance, while overlooking the spatial information required for robotic manipulation. To examine this limitation, we construct a simple Stride baseline that uniformly samples tokens along the flattened one-dimen...
Jia-Yu Chen, Shuyong Gao, Jing-Kai Jia et al.· 0 citations
QuantWAMs is presented, a PTQ framework that aligns quantization decisions with the calibration context defined by model structure, rollout distribution, and task objective, and co-training-objective saliency, which computes empirical-Fisher scores from the joint video--action gradient and assigns weight precision at a...
Jia-Chen Zhou, Jinfan Lv, Ruixuan Li et al.· arXiv.org· 0 citations
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