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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2601.05890 |
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| _version_ | 1866917192456470528 |
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| author | Zhang, Ruizhe Jiang, Xinke Yang, Zhibang Zhang, Zhixin Gao, Jiaran Xiao, Yuzhen Lai, Hongbin Chu, Xu Zhao, Junfeng Wang, Yasha |
| author_facet | Zhang, Ruizhe Jiang, Xinke Yang, Zhibang Zhang, Zhixin Gao, Jiaran Xiao, Yuzhen Lai, Hongbin Chu, Xu Zhao, Junfeng Wang, Yasha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05890 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management Zhang, Ruizhe Jiang, Xinke Yang, Zhibang Zhang, Zhixin Gao, Jiaran Xiao, Yuzhen Lai, Hongbin Chu, Xu Zhao, Junfeng Wang, Yasha Artificial Intelligence 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. |
| title | StackPlanner: A Centralized Hierarchical Multi-Agent System with Task-Experience Memory Management |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.05890 |