Title: BMAS: A Brain-Inspired Multi-Agent System with PFC-Guided Task Coordination and Hippocampus-Neocortex Dual Memory for Scalable Multi-Step Reasoning
Primary area: foundation or frontier models, including LLMs
Keywords: Multi-Agent Systems, Large Language Models, Brain-inspired, Hierarchical Task Decomposition, Memory Management

Abstract: Multi-Agent Systems (MAS) leveraging large language models have shown promise in tackling complex, multi-step tasks by distributing responsibilities across specialized agents. Recent research has introduced mechanisms such as explicit role assignment, hierarchical task allocation, and dynamic coordination strategies to improve agent collaboration and execution quality. However, most efforts focus separately on task scheduling or information sharing, overlooking the interplay between system-level architecture and memory management. This gap gives rise to two coupled challenges: static architectures limit adaptive coordination, which in turn amplifies the accumulation of long interaction histories, leading to inefficiency, instability, and degraded scalability.
Inspired by the human brain, we propose the Brain-inspired Multi-Agent System (BMAS), a principled framework that explicitly links architectural design with memory management. BMAS introduces a prefrontal cortex (PFC) like module responsible for hierarchical task decomposition, dynamic coordination, and working memory management, addressing the rigidity of conventional MAS architectures. Complementing this, a hippocampus-neocortex-inspired dual-memory system enables selective consolidation and semantic retrieval of task-relevant information, mitigating context inflation and enhancing backtracking capabilities.
We validate BMAS on diverse complex task datasets in mathematics and coding, demonstrating superior performance in accuracy, efficiency, and reasoning stability compared to existing MAS. Our approach bridges neuroscience insights and MAS design, providing a scalable framework for collaborative AI systems requiring long-horizon reasoning. The code is available at https://anonymous.4open.science/r/BMAS-AAD0.