Vision-and-Language Navigation (VLN) increasingly relies on large vision-language models, but their inference cost conflicts with real-time deployment. Token caching is a promising training-free strategy that avoids redundant computation by reusing stable visual tokens across frames. However, existing methods assume a static camera and fixed semantic focus, assumptions that VLN fundamentally violates. We identify two failure modes: (1) visual dynamics, where viewpoint shift displaces token positions across frames, causing position-wise matching to pair misaligned content; (2) semantic dynamics, where token relevance shifts across task stages as navigation progresses, making cached states stale. We propose VLN-Cache, a visual-dynamic-aware and semantic-dynamic-aware caching framework that introduces view-aligned remapping to recover geometric correspondences and a task-relevance saliency filter to veto reuse at semantic transitions. A layer-adaptive entropy policy further balances the per-layer reuse budget. Experiments on the R2R-CE simulation benchmark show up to 1.52x speedup while maintaining competitive navigation success rates.
@article{arxiv.2603.07080,
title = {VLN-Cache: Enabling Token Caching for VLN Models with Visual/Semantic Dynamics Awareness},
author = {Zihao Zheng and Zhihao Mao and Xingyue Zhou and Jiayu Chen and Maoliang Li and Xinhao Sun and Hailong Zou and Zhaobo Zhang and Xuanzhe Liu and Donggang Cao and Hong Mei and Xiang Chen},
journal= {arXiv preprint arXiv:2603.07080},
year = {2026}
}