DRAMA: Next-Gen Dynamic Orchestration for Resilient Multi-Agent Ecosystems in Flux
Zhao, Xinkui, Zhang, Yifan, Liu, Sai, Wang, Naibo, Cheng, Guanjie, Xu, Yueshen, Liu, Chang, Deng, Shuiguang 等 9 位作者
原文摘要
Embodied Multi-Agent Systems have proven highly effective in addressing complex tasks through coordinated collaboration among heterogeneous agents. However, real-world environments and task specifications are inherently dynamic, exhibiting frequent changes, uncertainty, and variability. Despite these characteristics, most existing frameworks employ static architectures with fixed agent capabilities and rigid task allocation strategies, which substantially constrain their adaptability to evolving conditions. This inflexibility presents significant challenges to maintaining robust and efficient multi-agent cooperation in dynamic and unpredictable settings.To address these limitations, we propose DRAMA, short for Dynamic Orchestration for Resilient Multi-Agent Ecosystems, tailored for rapidly changing environments. DRAMA adopts a multilayer architecture that incorporates three principal mechanisms: adaptive scheduling through an affinity-driven mechanism, fault-tolerant continuity via hierarchical trust-chain task takeover, and collective spatial intelligence that consolidates distributed observations for predictive reasoning. Together, these components enable event-triggered rescheduling and decentralized fault recovery, ensuring uninterrupted task execution amid agent arrivals, dropouts, or recoveries. Extensive experiments in the embodied VirtualHome-Social environment demonstrate that DRAMA achieves a 7% improvement in runtime efficiency and a 10% increase in throughput compared with state-of-the-art baselines, while maintaining superior stability and robustness under dynamic agent populations.
cs.CV · cs.MA
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表