StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management
This work addresses the challenges of long-horizon multi-agent collaboration in centralized systems, where ineffective memory management often leads to context bloat, error accumulation, and poor cross-task generalization. To overcome these limitations, we propose StackPlanner, a hierarchical framework that decouples high-level coordination from low-level task execution. StackPlanner introduces, for the first time, task-level active memory control combined with a reinforcement learning–based structured experience memory mechanism, enabling efficient retrieval and reuse of reusable collaborative experiences. Experimental results across multiple multi-agent benchmark tasks demonstrate that our approach significantly enhances the stability, efficiency, and generalization capability of long-horizon collaborative performance.