标题： SiLVR：一种基于语言的视频推理框架 显示英文标题

标题： SiLVR: A Simple Language-based Video Reasoning Framework

摘要： 测试时优化的最新进展使大型语言模型（LLMs）具备了显著的推理能力，使其能够解决高度复杂的数学和编码问题。然而，多模态大型语言模型（MLLMs）的推理能力仍明显滞后，尤其是在复杂的视频-语言任务方面。 为了解决这个问题，我们提出了SiLVR，一个基于简单语言的视频推理框架，将复杂的视频理解分解为两个阶段。 在第一阶段，SiLVR利用多感官输入（如短视频字幕和音频/语音字幕）将原始视频转换为基于语言的表示形式。 在第二阶段，语言描述被输入到强大的推理LLM中以解决复杂的视频-语言理解任务。 为了处理长上下文的多感官输入，我们采用了一种自适应令牌减少方案，该方案动态确定采样令牌的时间粒度。 我们的简单、模块化且无需训练的视频推理框架在Video-MME（长）、Video-MMMU（理解）、Video-MMLU、CGBench和EgoLife上取得了最佳报告的结果。 此外，我们的实证研究表明，尽管没有明确针对视频进行训练，强大的推理LLM可以有效地聚合来自视频、语音和音频的多感官输入信息，用于视频中的复杂时间、因果、长上下文和知识获取推理任务。 代码可在https://github.com/CeeZh/SILVR获取。 显示英文摘要

摘要： Recent advances in test-time optimization have led to remarkable reasoning capabilities in Large Language Models (LLMs), enabling them to solve highly complex problems in math and coding. However, the reasoning capabilities of multimodal LLMs (MLLMs) still significantly lag, especially for complex video-language tasks. To address this issue, we present SiLVR, a Simple Language-based Video Reasoning framework that decomposes complex video understanding into two stages. In the first stage, SiLVR transforms raw video into language-based representations using multisensory inputs, such as short clip captions and audio/speech subtitles. In the second stage, language descriptions are fed into a powerful reasoning LLM to solve complex video-language understanding tasks. To handle long-context multisensory inputs, we use an adaptive token reduction scheme, which dynamically determines the temporal granularity with which to sample the tokens. Our simple, modular, and training-free video reasoning framework achieves the best-reported results on Video-MME (long), Video-MMMU (comprehension), Video-MMLU, CGBench, and EgoLife. Furthermore, our empirical study focused on video reasoning capabilities shows that, despite not being explicitly trained on video, strong reasoning LLMs can effectively aggregate multisensory input information from video, speech, and audio for complex temporal, causal, long-context, and knowledge acquisition reasoning tasks in video. Code is available at https://github.com/CeeZh/SILVR.