arXiv 2606.19341 introduces OmniAgent, a 7B full-modality Agent that actively decides which parts of a video to focus on, beating 72B "watch-the-whole-video" models on long-video understanding benchmarks.
The "active perception" insight: long-video understanding is a "needle-in-a-haystack" task — the relevant information is usually concentrated in a few key frames. OmniAgent's Agent learns to "skim" through the video, identify the key frames, and only attend to them in detail. This is much more efficient than "watch everything."
The technical details: OmniAgent uses a "perception-policy" loop — the Agent observes a few frames, decides what to look at next, observes again, and so on. The "perception policy" is trained via reinforcement learning, with the reward being the final QA accuracy. The 7B Agent learns to be highly selective, focusing on 5-10% of frames for most videos.
The benchmark: on the VideoMME long-video benchmark (1-hour videos), OmniAgent-7B scores 76.4, beating Qwen2.5-VL-72B (74.1) and approaching GPT-5.6-Vision (78.2). The compute is 8× less than the 72B model.
The bigger takeaway: "active perception" is the right paradigm for long-video understanding. The "watch everything" approach is fundamentally inefficient, and the "skim + focus" approach scales much better. For the industry, this means long-video understanding products (surveillance, video search, video summarization) will see significant quality and cost improvements by adopting active perception.