Spatial reasoning has emerged as a critical capability for Multimodal Large Language Models (MLLMs), drawing increasing attention and rapid advancement. However, existing benchmarks primarily focus on single-step perception-to-judgment tasks, leaving scenarios requiring complex visual-spatial logical chains significantly underexplored. To bridge this gap, we introduce Video-MSR, the first benchmark specifically designed to evaluate Multi-hop Spatial Reasoning (MSR) in dynamic video scenarios. Video-MSR systematically probes MSR capabilities through four distinct tasks: Constrained Localization, Chain-based Reference Retrieval, Route Planning, and Counterfactual Physical Deduction. Our benchmark comprises 3,052 high-quality video instances with 4,993 question-answer pairs, constructed via a scalable, visually-grounded pipeline combining advanced model generation with rigorous human verification. Through a comprehensive evaluation of 20 state-of-the-art MLLMs, we uncover significant limitations, revealing that while models demonstrate proficiency in surface-level perception, they exhibit distinct performance drops in MSR tasks, frequently suffering from spatial disorientation and hallucination during multi-step deductions. To mitigate these shortcomings and empower models with stronger MSR capabilities, we further curate MSR-9K, a specialized instruction-tuning dataset, and fine-tune Qwen-VL, achieving a +7.82% absolute improvement on Video-MSR. Our results underscore the efficacy of multi-hop spatial instruction data and establish Video-MSR as a vital foundation for future research. The code and data will be available at https://github.com/ruiz-nju/Video-MSR.

翻译：空间推理已成为多模态大语言模型（MLLMs）的一项关键能力，正受到越来越多的关注并取得快速进展。然而，现有基准主要关注单步感知到判断的任务，对于需要复杂视觉-空间逻辑链的场景则探索明显不足。为弥补这一差距，我们提出了Video-MSR，这是首个专门用于评估动态视频场景中多跳空间推理（MSR）能力的基准。Video-MSR通过四个不同的任务系统性地探究MSR能力：约束定位、链式参照检索、路径规划和反事实物理推理。我们的基准包含3,052个高质量视频实例和4,993个问答对，通过一个可扩展、视觉接地的流水线构建，该流水线结合了先进模型生成与严格的人工验证。通过对20个最先进MLLM的全面评估，我们揭示了其显著局限性：尽管模型在表层感知方面表现出熟练度，但在MSR任务中却表现出明显的性能下降，在多步推理过程中经常遭受空间迷失和幻觉的困扰。为了缓解这些不足并赋予模型更强的MSR能力，我们进一步构建了MSR-9K，一个专门的指令微调数据集，并对Qwen-VL进行微调，在Video-MSR上实现了+7.82%的绝对性能提升。我们的结果证明了多跳空间指令数据的有效性，并将Video-MSR确立为未来研究的重要基础。代码和数据将在 https://github.com/ruiz-nju/Video-MSR 提供。