Video is temporally redundant: adjacent frames usually share most objects, background, and layout. Yet existing video multimodal large language models (video MLLMs) usually encode each sampled frame as an independent RGB image, causing visual tokens to repeat content already present in earlier frames. This suggests a more direct video interface: send a full reference frame only when the scene cannot be predicted well from prior context, and otherwise transmit a compact description of inter-frame changes. We call this interface a \emph{predictive visual code}, and instantiate it for video MLLMs as \textbf{AdaCodec}. AdaCodec spends full visual tokens on a reference frame only when its conditional predictive cost is high; otherwise, it encodes inter-frame changes, including motion and prediction residuals, as compact P-tokens. Across all eleven benchmarks, AdaCodec improves over the Qwen3-VL-8B per-frame RGB baseline at a matched visual-token budget. Even at $1/7$ the budget, AdaCodec with 32k tokens surpasses the 224k baseline on all long-video benchmarks; on five general-video benchmarks, it raises the average score while substantially cutting time-to-first-token from 9.26s to 1.62s.

翻译：视频在时间维度上存在冗余：相邻帧通常共享大部分物体、背景及布局。然而，现有视频多模态大语言模型通常将每帧采样独立编码为RGB图像，导致视觉标记重复先前帧已包含的内容。这提示了一种更直接的视频接口：仅当场景无法通过先前上下文有效预测时，才发送完整参考帧，否则传输帧间变化的紧凑描述。我们将该接口称为"预测性视觉编码"，并针对视频多模态大语言模型实例化为**AdaCodec**。AdaCodec仅在条件预测代价较高时为参考帧分配完整视觉标记，否则将包括运动与预测残差在内的帧间变化编码为紧凑的P标记。在全部十一个基准测试中，AdaCodec在匹配视觉标记预算条件下，优于基于Qwen3-VL-8B逐帧RGB的基线模型。即使在1/7预算下，采用32k标记的AdaCodec在所有长视频基准测试中仍超越224k基线；在五个通用视频基准测试中，其在提升平均得分的同时，将首帧生成时间从9.26秒大幅缩短至1.62秒。