Videos, with their unique temporal dimension, demand precise grounded understanding, where answers are directly linked to visual, interpretable evidence. Despite significant breakthroughs in reasoning capabilities within Large Language Models, multi-modal reasoning - especially for videos - remains unexplored. In this work, we introduce VideoMind, a novel video-language agent designed for temporal-grounded video understanding. VideoMind incorporates two key innovations: (i) We identify essential capabilities for video temporal reasoning and develop a role-based agentic workflow, including a planner for coordinating different roles, a grounder for temporal localization, a verifier to assess temporal interval accuracy, and an answerer for question-answering. (ii) To efficiently integrate these diverse roles, we propose a novel Chain-of-LoRA strategy, enabling seamless role-switching via lightweight LoRA adaptors while avoiding the overhead of multiple models, thus balancing efficiency and flexibility. Extensive experiments on 14 public benchmarks, including 3 on grounded video question-answering (Grounded VideoQA), 6 on video temporal grounding (VTG), and 5 on general video question-answering (VideoQA), verify that our agent achieves state-of-the-art performance on diverse video understanding tasks, underscoring its effectiveness in advancing video agent and long-form temporal reasoning.

翻译：视频因其独特的时序维度，需要精确的基于视觉证据的接地理解，即答案需直接关联到可解释的视觉证据。尽管大语言模型在推理能力方面取得了重大突破，但多模态推理——尤其是针对视频的推理——仍处于探索不足的状态。本文提出VideoMind，一种专为时序接地视频理解设计的新型视频-语言智能体。VideoMind包含两项关键创新：(i) 我们识别了视频时序推理所需的核心能力，并构建了一种基于角色的智能体工作流，包括用于协调不同角色的规划器、用于时序定位的接地器、用于评估时序区间准确性的验证器以及用于问答的应答器。(ii) 为高效整合这些多样化角色，我们提出了一种新颖的链式LoRA策略，通过轻量级LoRA适配器实现无缝角色切换，同时避免了多模型带来的开销，从而在效率与灵活性之间取得平衡。在14个公开基准上的大量实验（包括3个基于接地的视频问答基准、6个视频时序接地基准以及5个通用视频问答基准）验证了我们的智能体在多样化视频理解任务上达到了最先进的性能，凸显了其在推进视频智能体与长时序推理方面的有效性。