看视频不用反复思考，一次搞定
现在的AI看视频像侦探破案：反复搜索、推理、拼凑证据，又慢又费算力。Light-Omni反其道而行，它给AI装了两个“上下文记忆”：一个全局状态，像剧本一样把视频关键事件浓缩成有限长度的多模态脚本；另一个是参数化的潜在状态，直接从这个全局状态生成，驱动AI自主行动和检索，一次前向传播就搞定。结果：比最强视频智能体M3-Agent准确率高2.4%，速度快12.1倍，GPU内存效率提升2.6倍。它不是你明天就能用的工具，但指明了方向：让AI从“想太多”变成“看一眼就懂”。
📄 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.