NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning

Computer Science > Artificial Intelligence [Submitted on 16 May 2026] NeuroMAS: Multi-Agent Systems as Neural Networks with Joint Reinforcement Learning Haoran Lu, Luyang Fang, Wenxuan Zhong, Ping Ma 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 Focus to learn more Submission history From: Haoran Lu [view email] [v1] Sat, 16 May 2026 02:11:34 UTC (752 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.MA stat stat.ME stat.ML References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)