HiMemVLN: Enhancing Reliability of Open-Source Zero-Shot Vision-and-Language Navigation with Hierarchical Memory System

Kailin Lyu; Kangyi Wu; Pengna Li; Xiuyu Hu; Qingyi Si; Cui Miao; Ning Yang; Zihang Wang; Long Xiao; Lianyu Hu; Jingyuan Sun; Ce Hao

arXiv · 2603.14807

HiMemVLN: Enhancing Reliability of Open-Source Zero-Shot Vision-and-Language Navigation with Hierarchical Memory System

Kailin Lyu·Kangyi Wu·Pengna Li·Xiuyu Hu·Qingyi Si·Cui Miao·Ning Yang·Zihang Wang·Long Xiao·Lianyu Hu·Jingyuan Sun·Ce Hao

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Abstract

LLM-based agents have demonstrated impressive zero-shot performance in vision-language navigation (VLN) tasks. However, most zero-shot methods primarily rely on closed-source LLMs as navigators, which face challenges related to high token costs and potential data leakage risks. Recent efforts have attempted to address this by using open-source LLMs combined with a spatiotemporal CoT framework, but they still fall far short compared to closed-source models. In this work, we identify a critical issue, Navigation Amnesia, through a detailed analysis of the navigation process. This issue leads to navigation failures and amplifies the gap between open-source and closed-source methods. To address this, we propose HiMemVLN, which incorporates a Hierarchical Memory System into a multimodal large model to enhance visual perception recall and long-term localization, mitigating the amnesia issue and improving the agent's navigation performance. Extensive experiments in both simulated and real-world environments demonstrate that HiMemVLN achieves nearly twice the performance of the open-source state-of-the-art method. The code is available at https://github.com/lvkailin0118/HiMemVLN.

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Kailin Lyu, Kangyi Wu, Pengna Li, Xiuyu Hu, Qingyi Si, Cui Miao, Ning Yang, Zihang Wang, Long Xiao, Lianyu Hu, Jingyuan Sun, Ce Hao. 2026-03-16. HiMemVLN: Enhancing Reliability of Open-Source Zero-Shot Vision-and-Language Navigation with Hierarchical Memory System. https://arxiv.org/abs/2603.14807

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