视频 MLLM 开发者终于有了专门诊断时间保真度的基准——Moment-Video 直击模型在瞬间事件上的致命短板，做视频理解或模型评估的团队值得用它来检验自家模型。
视频多模态大语言模型在长视频理解上进步迅速，但它们在捕捉短暂但关键的视觉证据（如几帧内的动作或状态变化）方面能力不足。Moment-Video 是一个新基准，包含 1000 个人工验证的视频问答对，覆盖 7 个领域和 25 个子类别，测试模型在时间发生、计数、动作描述和推理上的表现。评估 33 个模型后，最佳模型 Seed-2.0-Pro 准确率仅 39.6%，多数开源模型低于 25%，揭示了巨大差距。分析表明，密集帧采样能部分改善但无法消除瓶颈，长视频带来更强的定位挑战。这显示当前视频 MLLM 仍缺乏时间保真表示来捕捉和利用短暂但决定性的视觉证据。
Moment-Video: Diagnosing Temporal Fidelity of Video MLLMs on Momentary Visual Events
Video multimodal large language models (MLLMs) have made rapid progress on general and long-form video understanding, yet their ability to preserve brief answer-critical visual evidence remains underexplored. Many practical questions are determined by momentary visual events: localized actions or state transitions that may last only a few frames. Such evidence can be skipped by sparse frame sampling, suppressed by visual-token compression, or diluted by coarse temporal aggregation, causing failures that language-side reasoning cannot reliably recover. We introduce Moment-Video, a benchmark for diagnosing the temporal fidelity of video MLLMs through momentary visual event understanding. Each question is grounded in a localized, visually observable, and sampling-sensitive event, requiring models to notice, count, describe, or reason about transient evidence rather than rely on persistent objects, global scene context, or language priors. Moment-Video contains 1,000 human-verified video-QA pairs across 7 domains and 25 fine-grained subcategories, covering four task types: Temporal Occurrence, Temporal Counting, Action Description, and Temporal Reasoning. We evaluate 33 proprietary and open-source MLLMs on Moment-Video. The best-performing model, Seed-2.0-Pro, achieves only 39.6% overall accuracy, while most open-source models remain below 25%, revealing a substantial gap in momentary visual event understanding. Diagnostic analyses show that denser frame sampling improves some models but does not eliminate the bottleneck, and longer videos introduce stronger temporal-localization challenges. These findings suggest that current video MLLMs still lack temporally faithful representations for capturing, preserving, and using brief but decisive visual evidence.