Multi-agent systems based on large language models, particularly centralized architectures, have recently shown strong potential for complex and knowledge-intensive tasks. However, central agents often suffer from unstable long-horizon collaboration due to the lack of memory management, leading to context bloat, error accumulation, and poor cross-task generalization. To address both task-level memory inefficiency and the inability to reuse coordination experience, we propose StackPlanner, a hierarchical multi-agent framework with explicit memory control. StackPlanner addresses these challenges by decoupling high-level coordination from subtask execution with active task-level memory control, and by learning to retrieve and exploit reusable coordination experience via structured experience memory and reinforcement learning. Experiments on multiple deep-search and agent system benchmarks demonstrate the effectiveness of our approach in enabling reliable long-horizon multi-agent collaboration.

翻译：基于大语言模型的多智能体系统，尤其是集中式架构，近年来在处理复杂且知识密集型任务方面展现出巨大潜力。然而，由于缺乏记忆管理，中心智能体常常面临长时程协作不稳定的问题，导致上下文膨胀、错误累积以及跨任务泛化能力差。为了解决任务级记忆效率低下和无法复用协调经验这两大问题，我们提出了StackPlanner，一个具有显式记忆控制的分层多智能体框架。StackPlanner通过主动的任务级记忆控制将高层协调与子任务执行解耦，并通过结构化的经验记忆和强化学习来学习检索和利用可复用的协调经验，从而应对这些挑战。在多个深度搜索和智能体系统基准测试上的实验证明了我们的方法在实现可靠的长时程多智能体协作方面的有效性。