做零样本视频空间推理的团队终于有了一个即插即用的提升方案——SpatioRoute 无需额外训练就能涨点 5%，做 VLM 应用的开发者可以直接在 SQA3D 上试试。
SpatioRoute 是一种无需训练或微调的动态提示生成方法，用于提升视觉语言模型在零样本设置下对第一人称视频的空间问答能力。它通过规则或大语言模型驱动的路由，将每个问题映射到语义定制的提示模板，在 SQA3D 基准上相比固定提示基线提升高达 5% 的准确率，且无需 3D 点云输入。研究还发现，链式思维提示在 Qwen 系列模型上反而会降低性能，表明问题感知路由比统一推理指令更有效。
SPATIOROUTE: Dynamic Prompt Routing for Zero-Shot Spatial Reasoning
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.