Spatial question answering over egocentric video is a challenging task that requires Vision-Language Models (VLMs) to reason about 3D object positions, scene affordances, and directional relationships, particularly in the zero-shot setting where no task-specific fine-tuning is available. We introduce SpatioRoute, a dynamic prompt generation approach that routes each incoming question to a semantically tailored prompt template -- without any additional training, fine-tuning, or 3D sensor input. SpatioRoute operates in two complementary modes: SpatioRoute-R, a rule-based router that deterministically maps question typologies (e.g., What, Is, How, Can, Which) to specialized prompt templates; and SpatioRoute-L, an LLM-driven approach that generates task-specific prompts from the question and situational context alone, with no video input at routing time. We evaluate SpatioRoute on the SQA3D benchmark across VLMs spanning model families. SpatioRoute achieves consistent overall accuracy gains up to 5% over fixed prompt baselines, establishing a new state-of-the-art for zero-shot video-only spatial VQA without requiring 3D point-cloud inputs. As an additional finding, we observe that Chain-of-Thought (CoT) prompting, implemented via the Think it Twice architecture, consistently degrades performance in this setting on Qwen series models, confirming that question-aware routing is more effective than uniform reasoning instructions for spatial video understanding.

翻译：对自我中心视频进行空间问答是一项具有挑战性的任务，要求视觉语言模型推理三维物体位置、场景可供性与方向关系——尤其在没有任务特定微调的零样本设定下。我们提出SpatioRoute，一种动态提示生成方法，将每个输入问题路由至语义定制的提示模板，无需额外训练、微调或三维传感器输入。SpatioRoute以两种互补模式运作：SpatioRoute-R，一种基于规则的路由器，将问题类型（例如“是什么”“是否”“如何”“能否”“哪一个”）确定性映射至专用提示模板；以及SpatioRoute-L，一种由大语言模型驱动的方法，仅依据问题和情境上下文生成任务特定提示，路由阶段无需视频输入。我们在涵盖多种模型家族的视觉语言模型上，基于SQA3D基准评估SpatioRoute。与固定提示基线相比，SpatioRoute实现了一致且最高达5%的整体准确率提升，在无需三维点云输入的情况下，为纯视频零样本空间视觉问答建立了新的最优结果。作为额外发现，我们观察到通过“思虑两次”架构实现的思维链提示，在Qwen系列模型中会持续降低该设定下的性能，从而证实对于空间视频理解，问题感知路由优于统一推理指令。