We introduce UCS-Bench, a dataset spanning 170+ hours of egocentric visual observations with 8.1K+ timestamped questions for diagnosing User-Centric Continual Spatial intelligence in egocentric video streams. UCS-Bench targets a new problem that emphasizes dynamic spatial reasoning, long-term memory, and their alignment with users' real-time locations. We propose DirectMe, a framework that incrementally constructs and maintains a structured spatial memory from streaming egocentric observations. DirectMe enables robust tracking and recall of object locations, all relative to the user's movement over time. By tightly coupling visual perception with memory updates and spatial reasoning, our approach supports long-horizon queries that require recalling interactions, resolving viewpoint-induced ambiguities, and adapting to dynamic scenes. Our experiments show that DirectMe significantly improves the spatial reasoning of leading multimodal LLMs; it also surpasses many spatially aware and long-form streaming video models. We hope our benchmark and solution will advance spatial intelligence research for egocentric AI assistants. Data and code are available at https://github.com/cocowy1/UCS-Bench.

翻译：我们提出了UCS-Bench数据集，该数据集包含超过170小时的自我中心视觉观测数据及8.1K+个带时间戳的问题，用于诊断自我中心视频流中面向用户中心的持续空间智能问题。UCS-Bench针对一个新问题，强调动态空间推理、长期记忆及其与用户实时位置的校准。我们提出DirectMe框架，该框架通过增量式构建和维护来自流式自我中心观测的结构化空间记忆。DirectMe能够稳健追踪和回忆物体位置，并始终与用户随时间移动的相对位置保持关联。通过将视觉感知与记忆更新及空间推理紧密耦合，我们的方法支持需回溯交互的长程查询、解决视角引发的歧义，并适应动态场景。实验表明，DirectMe显著提升了主流多模态大语言模型的空间推理能力；其表现也优于多个空间感知及长时流式视频模型。我们期望该基准与解决方案能推动面向自我中心AI助手的空间智能研究。数据和代码已开源至 https://github.com/cocowy1/UCS-Bench。