标题： CoV：用于空间推理的视角链提示 显示英文标题

标题： CoV: Chain-of-View Prompting for Spatial Reasoning

摘要： 具身问答（EQA）在3D环境中通常需要收集分布在多个视角且部分遮挡的上下文。然而，大多数最新的视觉-语言模型（VLMs）被限制在一个固定且有限的输入视角集合中，这限制了它们在推理时获取与问题相关的上下文的能力，并阻碍了复杂的空间推理。我们提出了视图链（CoV）提示，这是一种无需训练的测试时推理框架，通过粗到细的探索过程将VLM转变为活动视角推理器。CoV首先使用一个视角选择代理来过滤冗余帧并识别与问题对齐的锚点视角。然后通过将迭代推理与离散相机动作交错进行精细的视角调整，从底层3D场景表示中获得新的观察结果，直到收集到足够的上下文或达到步骤预算。我们在OpenEQA上对CoV进行了评估，四种主流VLMs的LLM-Match平均提高了+11.56%，其中在Qwen3-VL-Flash上的最大提升为+13.62%。CoV还表现出测试时的扩展性：增加最小动作预算可带来额外的+2.51%平均提升，在Gemini-2.5-Flash上达到+3.73%。在ScanQA和SQA3D上，CoV表现出色（例如，在ScanQA上获得116 CIDEr / 31.9 EM@1，在SQA3D上获得51.1 EM@1）。总体而言，这些结果表明，与开放视角搜索相结合的问题对齐视角选择是一种有效的方法无关策略，可以在不进行额外训练的情况下提高3D EQA中的空间推理能力。代码可在https://github.com/ziplab/CoV 上获得。 显示英文摘要

摘要： Embodied question answering (EQA) in 3D environments often requires collecting context that is distributed across multiple viewpoints and partially occluded. However, most recent vision--language models (VLMs) are constrained to a fixed and finite set of input views, which limits their ability to acquire question-relevant context at inference time and hinders complex spatial reasoning. We propose Chain-of-View (CoV) prompting, a training-free, test-time reasoning framework that transforms a VLM into an active viewpoint reasoner through a coarse-to-fine exploration process. CoV first employs a View Selection agent to filter redundant frames and identify question-aligned anchor views. It then performs fine-grained view adjustment by interleaving iterative reasoning with discrete camera actions, obtaining new observations from the underlying 3D scene representation until sufficient context is gathered or a step budget is reached. We evaluate CoV on OpenEQA across four mainstream VLMs and obtain an average +11.56% improvement in LLM-Match, with a maximum gain of +13.62% on Qwen3-VL-Flash. CoV further exhibits test-time scaling: increasing the minimum action budget yields an additional +2.51% average improvement, peaking at +3.73% on Gemini-2.5-Flash. On ScanQA and SQA3D, CoV delivers strong performance (e.g., 116 CIDEr / 31.9 EM@1 on ScanQA and 51.1 EM@1 on SQA3D). Overall, these results suggest that question-aligned view selection coupled with open-view search is an effective, model-agnostic strategy for improving spatial reasoning in 3D EQA without additional training. Code is available on https://github.com/ziplab/CoV .