StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management
This addresses memory inefficiency and poor generalization in multi-agent systems, but appears incremental as it builds on existing hierarchical and memory-based approaches.
The paper tackled the problem of unstable long-horizon collaboration in centralized multi-agent systems due to poor memory management, proposing StackPlanner, which improved performance on multiple benchmarks.
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.