ADAPT: Auction-Based Dynamic Prioritization for Multi-Agent Coordination

Effective coordination in multi-agent systems remains challenging in dynamic and partially observable environments, where agents must reason over evolving interdependencies and limited communication bandwidth. We propose ADAPT, a unified framework for multi-agent coordination that integrates message compression, dependency estimation, and a novel auction-based dynamic prioritization mechanism. In ADAPT, agents exchange compact messages and compute dependency scores to determine how much their behavior depends on others. A distributed auction protocol then assigns priority positions, guiding autoregressive decision-making in a manner aligned with inter-agent influence. This enables flexible, influence-aware coordination without centralized control or extensive communication rounds. Experiments on SMACv2 and GRF show that ADAPT achieves higher win rates, faster convergence, and lower communication cost compared to state-of-the-art baselines. Further analyses confirm its scalability to large teams, compatibility with value decomposition, and runtime efficiency. These results show that ADAPT enables scalable, efficient, and modular multi-agent coordination.

Authors

• Zaipeng Xie
• Chentai Qiao
• Nuo Yang
• Yiming Zhao

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