Training-free Vision-Language Navigation (VLN) agents powered by foundation models can follow instructions and explore 3D environments. However, existing approaches rely on greedy frontier selection and passive spatial memory, leading to inefficient behaviors such as local oscillation and redundant revisiting. We argue that this stems from a lack of metacognitive capabilities: the agent cannot monitor its exploration progress, diagnose strategy failures, or adapt accordingly. To address this, we propose MetaNav, a metacognitive navigation agent integrating spatial memory, history-aware planning, and reflective correction. Spatial memory builds a persistent 3D semantic map. History-aware planning penalizes revisiting to improve efficiency. Reflective correction detects stagnation and uses an LLM to generate corrective rules that guide future frontier selection. Experiments on GOAT-Bench, HM3D-OVON, and A-EQA show that MetaNav achieves state-of-the-art performance while reducing VLM queries by 20.7%, demonstrating that metacognitive reasoning significantly improves robustness and efficiency.

翻译：基于基础模型的无训练视觉-语言导航（VLN）智能体能够执行指令并探索三维环境。然而，现有方法依赖贪婪的前沿选择策略和被动式空间记忆，导致局部振荡和重复访问等低效行为。我们认为，这源于元认知能力的缺失：智能体无法监控探索进度、诊断策略失败或自适应调整。为此，我们提出MetaNav——一种融合空间记忆、历史感知规划与反思校正的元认知导航智能体。空间记忆构建持久的三维语义地图，历史感知规划通过惩罚重复访问提升效率，反思校正检测停滞状态并利用大语言模型生成校正规则以指导后续前沿选择。在GOAT-Bench、HM3D-OVON和A-EQA数据集上的实验表明，MetaNav在取得最先进性能的同时，将VLM查询次数减少20.7%，证实了元认知推理可显著提升鲁棒性与效率。