Traditional artificial intelligence acts like a passive viewer, often processing every single frame of a long video regardless of relevance. This inefficiency drains battery life and computing resources while missing the nuance required for complex tasks. A new system called EVA changes this game by introducing planning before perception, allowing the agent to autonomously select which scenes matter most. Instead of watching everything, it iterates through a loop of summarizing plans, taking actions, and reflecting on results. To build this, engineers combined three distinct training stages that bridge the gap between simple imitation and complex reinforcement learning. The results are striking: EVA delivers a 6 to 12 percent boost over standard large language models and an additional 1 to 3 percent improvement over previous adaptive agents. This shift from uniform sampling to strategic observation marks a major leap in making video understanding both smarter and faster.

EVA: Efficient Reinforcement Learning for End-to-End Video Agent by Yaolun Zhang et al., https://arxiv.org/abs/2603.22918