标题： StreamVLN：通过SlowFast上下文建模的流式视觉-语言导航 显示英文标题

标题： StreamVLN: Streaming Vision-and-Language Navigation via SlowFast Context Modeling

摘要： 视觉与语言导航（VLN）在现实场景中需要智能体处理连续的视觉流，并根据语言指令生成低延迟的动作。 虽然基于视频的大规模语言模型（Video-LLMs）推动了近期的进步，但基于Video-LLM的当前VLN方法在细粒度视觉理解、长期上下文建模和计算效率之间常常面临权衡。 我们引入了StreamVLN，这是一种流式VLN框架，采用混合的慢速-快速上下文建模策略，以支持交错的视觉、语言和动作输入的多模态推理。 快速流式对话上下文通过活动对话的滑动窗口促进响应式动作生成，而慢速更新的记忆上下文则通过3D感知的标记剪枝策略压缩历史视觉状态。 通过这种慢速-快速设计，StreamVLN通过高效的键值缓存重用实现了连贯的多轮对话，支持具有有限上下文大小和推理成本的长视频流。 在VLN-CE基准上的实验表明，其性能处于最先进水平，且具有稳定的低延迟，确保了现实部署中的鲁棒性和效率。 项目页面是： \href{https://streamvln.github.io/}{https://streamvln.github.io/} 显示英文摘要

摘要： Vision-and-Language Navigation (VLN) in real-world settings requires agents to process continuous visual streams and generate actions with low latency grounded in language instructions. While Video-based Large Language Models (Video-LLMs) have driven recent progress, current VLN methods based on Video-LLM often face trade-offs among fine-grained visual understanding, long-term context modeling and computational efficiency. We introduce StreamVLN, a streaming VLN framework that employs a hybrid slow-fast context modeling strategy to support multi-modal reasoning over interleaved vision, language and action inputs. The fast-streaming dialogue context facilitates responsive action generation through a sliding-window of active dialogues, while the slow-updating memory context compresses historical visual states using a 3D-aware token pruning strategy. With this slow-fast design, StreamVLN achieves coherent multi-turn dialogue through efficient KV cache reuse, supporting long video streams with bounded context size and inference cost. Experiments on VLN-CE benchmarks demonstrate state-of-the-art performance with stable low latency, ensuring robustness and efficiency in real-world deployment. The project page is: \href{https://streamvln.github.io/}{https://streamvln.github.io/}.