Towards Effective Long Video Understanding: Dynamic MAS Construction via Meta-Agent

Abstract

Long video understanding is fundamentally constrained by massive data volume and high information density. While agent-based methods mitigate context length bottlenecks, they predominantly rely on static, "one-size-fits-all" multi-agent systems (MAS). These rigid architectures are computationally redundant and lack the flexibility to handle diverse, instance-level reasoning demands. To address this, we propose DyMAC (Dynamic MAS Construction), a novel framework that dynamically generates instance-specific MAS for each video-question pair. By logically abstracting the MAS into a directed computational graph via automated code generation, DyMAC exhibits exceptional adaptability, seamlessly tailoring its collaborative architecture to the specific complexity of each input. Furthermore, we employ a streamlined two-stage training strategy—Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR)—to enhance the model’s dynamic construction and decision-making capabilities. Extensive experiments across four mainstream benchmarks demonstrate that DyMAC achieves a new state-of-the-art, outperforming the second-best method by 3.3% on average. Notably, it significantly improves inference efficiency compared to existing static approaches, further highlighting its superior adaptability and robustness in complex video scenarios.