AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917054092673024 |
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| author | Wang, Song Tan, Zhen Chen, Zihan Zhou, Shuang Chen, Tianlong Li, Jundong |
| author_facet | Wang, Song Tan, Zhen Chen, Zihan Zhou, Shuang Chen, Tianlong Li, Jundong |
| contents | Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existing methods largely rely on static or graph-based inter-agent topologies, lacking the potential adaptability and flexibility in communication. In this work, we propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure, offering a significantly larger topology space for multi-agent communication. Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection (NCS), which enables each agent to selectively access relevant information from any previous step. Together, these components construct task-adaptive communication pipelines that support both role flexibility and global information flow. Extensive evaluations across multiple benchmarks demonstrate that our approach achieves superior performance while substantially reducing communication overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_17784 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction Wang, Song Tan, Zhen Chen, Zihan Zhou, Shuang Chen, Tianlong Li, Jundong Artificial Intelligence Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existing methods largely rely on static or graph-based inter-agent topologies, lacking the potential adaptability and flexibility in communication. In this work, we propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure, offering a significantly larger topology space for multi-agent communication. Our method focuses on two key directions: (1) Next-Agent Prediction, which selects the most suitable agent role at each step, and (2) Next-Context Selection (NCS), which enables each agent to selectively access relevant information from any previous step. Together, these components construct task-adaptive communication pipelines that support both role flexibility and global information flow. Extensive evaluations across multiple benchmarks demonstrate that our approach achieves superior performance while substantially reducing communication overhead. |
| title | AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2506.17784 |