NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning
NeuroMAS introduces a novel approach to multi-agent language systems by treating them as neural networks with joint reinforcement learning. This method allows for scalable and trainable architectures where agents can communicate and specialize without predefined roles. Experimental results indicate that NeuroMAS outperforms traditional multi-agent systems, suggesting a promising direction for enhancing large language models.
- ▪NeuroMAS treats multi-agent systems as neural networks, enabling scalable architectures.
- ▪The method allows agents to communicate and specialize without predefined roles.
- ▪Experiments show significant improvements over traditional multi-agent baselines.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.16757 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.16757 (cs) [Submitted on 16 May 2026] Title:NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning Authors:Haoran Lu, Luyang Fang, Wenxuan Zhong, Ping Ma View a PDF of the paper titled NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning, by Haoran Lu and 3 other authors View PDF HTML (experimental) Abstract:Multi-agent language systems are often built as hand-designed workflows, where agents are assigned semantic roles and communication protocols are specified in advance. We propose NeuroMAS, a method that first treats a multi-agent language system as a trainable and scalable neural-network-like architecture with LLM agents as nodes and intermediate textual signals as edges.
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