AI看视频不再“绕弯子”：快12倍还更准
现在的AI看视频像侦探破案：每看一段就要停下来推理、搜索、拼凑线索，又慢又费算力。这篇论文反其道而行之——让AI像人一样“瞟一眼”就懂。研究者给AI装了两个“记忆抽屉”：一个全局抽屉，把视频里发生的事自动压缩成一本“小剧本”，保留关键细节、概括旧情节；另一个是条件反射抽屉，基于剧本直接生成下一步动作和检索关键词，不再反复推理。结果：在多个视频理解测试中，准确率比当前最强系统高2.4%，速度快12倍，显存占用少一半多。它不是你明天就能用的产品，但指明了方向：让AI少想、多记，反而更聪明。
📄 原文摘要(英文)
Agentic video understanding equips models with long-term memory to autonomously process and respond to continuous, long-horizon multimodal streams. However, advanced video agents often rely on ``detective-style'' iterative reasoning for action control (e.g., search) and evidence aggregation, incurring prohibitive costs and latency. We argue that such heavy reasoning primarily compensates for the lack of global context and semantic misalignment in retrieval. This paper introduces Light-Omni, a multimodal agent framework for reflexive and lightweight video understanding. It achieves this through dual contextual states that instantly build the required context in a single forward pass. First, we maintain a global state, a finite-sized multimodal script continuously consolidated from episodic memory, serving as the global context for Light-Omni. Through hierarchical merging, it preserves recent details while summarizing past events. Second, conditioned on this global context, we generate a parametric latent state that directly drives autonomous actions and produces retrieval embeddings, with minimal latency. Benefiting from this coupled design, Light-Omni achieves semantically aligned retrieval and reflexive responses while avoiding iterative reasoning. Extensive experiments validate the effectiveness of Light-Omni across multiple video benchmarks. Notably, it outperforms M3-Agent with an average 2.4% accuracy gain, a 12.1times speedup, and a 2.6times improvement in GPU memory efficiency. Furthermore, it serves as a memory system to enhance both the performance and efficiency of existing MLLMs. Project page: https://clare-nie.github.io/Light-Omni.