发表机构
The Hong Kong University of Science and Technology; The Hong Kong University of Science and Technology (Guangzhou); National University of Singapore; Zhejiang University（香港科技大学; 香港科技大学（广州）; 新加坡国立大学; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对动态视觉语言导航中语言推理慢与规划需即时的矛盾，提出SPARK-VLN双系统框架，通过三个模块将慢速VLM推理器知识流到快速规划器，引入新基准套件，提高了导航成功率、社会合规性及推理效率。
AI 中文摘要
在动态、以人类为中心的环境中的视觉语言导航存在一个基本矛盾：语言推理缓慢且深思熟虑，而安全、符合社会规范的规划应该即时且具有反应性。由此产生的观测陈旧性对安全至关重要：推理过程中选择的动作在执行时可能已经不安全。我们观察到，在VLM完成推理之前很久，其中间隐藏状态就已经编码了与动作相关的意图。我们提出了SPARK-VLN，这是一个用于动态社会VLN的双系统框架，在整个令牌生成过程中将慢速VLM推理器的知识流到快速流匹配专家规划器，在推理过程中提供新的和不断演变的指导。该设计由三个模块实现：一个逐令牌隐藏流提取器，一个序列到插槽潜在桥接器，一个不断演变的潜在调节器。我们还引入了一个用于动态社会视觉语言导航的以人类为中心的基准套件，该套件在整个推理过程中使行人和机器人保持活跃，并报告导航成功、社会合规性、人类碰撞和明确的陈旧性统计数据。在这些设置中，SPARK-VLN提高了导航成功率和社会合规性，并保持了推理效率。
英文摘要
Vision-Language Navigation in dynamic, human-centric environments exposes a fundamental tension: linguistic reasoning is slow and deliberative, whereas safe, socially compliant planning should be instant and reactive. The resulting observation staleness is safety-critical: a maneuver chosen during inference can already be unsafe by the time it executes. We observe that, long before a VLM finishes its inference, its intermediate hidden states already encode action-relevant intent. We propose SPARK-VLN, a dual-system framework for dynamic social VLN that streams the slow VLM reasoner's knowledge to a fast flow-matching expert planner throughout token generation, providing fresh and evolving guidance during inference. This design is realized by three modules: a Token-Wise Hidden Streamer that extracts intermediate hidden states along the token generation process, a Sequence-to-Slot Latent Bridge that projects them into fixed-size latent slots, and an Evolving Latent Conditioner that infuses them into the expert planner. We also introduce a human-centric benchmark suite for dynamic social vision-language navigation that keeps pedestrians and the robot active throughout inference and reports navigation success, social compliance, human collisions, and explicit staleness statistics. Across these settings, SPARK-VLN mproves navigation success and social compliance while sustaining inference efficiency. Webpage: https://hutslib.github.io/SPARK-VLN/.
