NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning
Quick Answer
NeuroMAS proposes a scalable neural-network-like architecture for multi-agent systems, utilizing LLM agents as nodes and reinforcement learning for communication.
Quick Take
This approach enhances parameter efficiency and performance, showing significant improvements over traditional baselines, particularly when scaling from smaller systems.
Key Points
- NeuroMAS treats multi-agent systems as a trainable neural network architecture.
- Agent nodes are role-free but aware of structural information flow.
- The method shows significant performance improvements over existing multi-agent systems.
- Organizational scaling is path-dependent, favoring progressive growth from smaller systems.
- Theoretical insights suggest modular textual computation is more parameter-efficient.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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, where depth, width, connectivity, and growth protocol become scalable sources of capability. Further, we provide a theoretical perspective showing why such modular textual computation is more parameter-efficient when tasks admit hierarchical decompositions. Experiments show that NeuroMAS improves significantly over both inference-time and trained multi-agent baselines. We further find that organizational scaling is path-dependent: larger systems can be challenging to train from scratch, but become feasible when grown progressively from smaller trained systems. These results suggest that learned neural multi-agent systems are a promising scaling axis for LLMs.
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Methodology (stat.ME); Machine Learning (stat.ML) |
| Cite as: | arXiv:2605.16757 [cs.AI] |
| (or arXiv:2605.16757v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16757 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Haoran Lu [view email]
[v1]
Sat, 16 May 2026 02:11:34 UTC (752 KB)
— Originally published at arxiv.org
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