NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning
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. In NeuroMAS, agent nodes are role-free but structure-aware: the topology only determines how information can flow in general, while reinforcement learning training determines how nodes communicate, specialize, and coordinate. This formulation shifts multi-agent design from workflow engineering toward architecture design,...
| Authors | Haoran Lu, Luyang Fang, Wenxuan Zhong et al. |
|---|---|
| Primary category | cs.AI |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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