Abstract
A short video succeeds not simply because of what it shows, but because of how it schedules attention -- yet current multimodal models lack the structural grammar to parse or produce this organization. Existing models can describe scenes, answer event-centric questions, and read on-screen text, but they are far less reliable at identifying timeline-grounded units such as hooks, cut rationales, shot-induced tension, and platform-facing packaging cues. We propose SV6D (Structured Video in Six Dimensions), inspired by professional storyboard practice in film and television production, a representation framework that decomposes internet-native video into six complementary structural dimensions -- subject, aesthetics, camera language, editing, narrative, and dissemination -- with each label tied to physically observable evidence on the timeline. We formalize a unified optimization objective over SV6D that combines Hungarian-matched temporal alignment, dimension-wise semantic label distance, and quality regularization. Building on this framework, we present Leum-VL-8B, an 8B video-language model that realizes the SV6D objective through an expert-driven post-training pipeline, further refined through verifiable reinforcement learning on perception-oriented tasks. Leum-VL-8B achieves 70.8 on VideoMME (w/o subtitles), 70.0 on MVBench, and 61.6 on MotionBench, while remaining competitive on general multimodal evaluations such as MMBench-EN. We also construct FeedBench, a benchmark for structure-sensitive short-video understanding. Our results indicate that the missing layer in video AI is not pixel generation but structural representation: grounded on the timeline, linked to visible evidence, and directly consumable by downstream workflows such as editing, retrieval, recommendation, and generation control, including text-heavy internet video formats with overlays and image-text layouts.
Abstract (translated)
短视频的成功不仅在于其展示的内容，更在于其如何调度注意力——然而当前多模态模型缺乏解析或生成这种组织所需的结构性语法。现有模型能够描述场景、回答以事件为中心的问题并阅读屏幕文本，但在识别基于时间线的单元（如钩子、剪辑依据、镜头引发的张力以及面向平台的包装信号）方面远不可靠。我们提出SV6D（六维结构化视频），其灵感来自影视制作中的专业故事板实践，该表示框架将互联网原生视频分解为六个互补的结构维度——主体、美学、镜头语言、剪辑、叙事与传播——每个标签均与时间线上可观察的物理证据相关联。我们形式化了结合匈牙利匹配的时间对齐、维度级语义标签距离和质量正则化的统一优化目标。基于此框架，我们推出了Leum-VL-8B，一个8B参数的视频-语言模型，它通过专家驱动的后训练流水线实现了SV6D目标，并进一步通过面向感知任务的可验证强化学习进行精炼。Leum-VL-8B在VideoMME（无字幕）上达到70.8，在MVBench上达到70.0，在MotionBench上达到61.6，同时在MMBench-EN等通用多模态评估中仍具竞争力。我们还构建了FeedBench，一个对结构敏感的短视频理解基准。我们的结果表明，视频AI中缺失的层面并非像素生成，而是结构性表示：它基于时间线、关联可见证据，并可直接被编辑、检索、推荐和生成控制等下游工作流消费，包括带有叠加层和图文排版的文本密集型互联网视频格式。
URL
https://arxiv.org/abs/2603.20354
