Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of language tasks, yet complex multi-step reasoning remains a fundamental challenge. While Large Reasoning Models (LRMs) equipped with extended chain-of-thought mechanisms demonstrate improved performance over standard LLMs, both model types still suffer from accuracy collapse on sufficiently complex tasks, suggesting that scaling model-level reasoning alone is insufficient. Inspired by the global workspace theory of human cognition, we propose Brain-Inspired Graph Multi-Agent Systems (BIGMAS), in which specialized LLM agents are organized as nodes in a dynamically constructed directed graph and coordinate exclusively through a centralized shared workspace. A problem-adaptive GraphDesigner constructs task-specific agent topologies, while a global Orchestrator leverages the complete shared state for routing decisions, overcoming the local-view bottleneck of reactive approaches. Experiments on Game24, Six Fives, and Tower of London across six frontier LLMs demonstrate that BIGMAS consistently improves reasoning performance for both standard LLMs and LRMs, outperforming existing multi-agent baselines including ReAct and Tree of Thoughts, showing that multi-agent architectural design provides complementary gains orthogonal to model-level reasoning enhancements.

翻译：大语言模型（LLMs）已在广泛的语言任务中展现出卓越能力，但复杂的多步推理仍然是一个根本性挑战。尽管配备扩展思维链机制的大型推理模型（LRMs）相比标准LLMs表现出性能提升，但这两类模型在足够复杂的任务上仍会遭遇准确率崩溃，这表明仅靠模型级推理的扩展是不够的。受人类认知的全局工作空间理论启发，我们提出大脑启发的图多智能体系统（BIGMAS），其中专用LLM智能体被组织为动态构建的有向图中的节点，并仅通过一个中心化的共享工作空间进行协调。一个适应问题的GraphDesigner构建任务特定的智能体拓扑，而全局Orchestrator则利用完整的共享状态进行路由决策，从而克服了反应式方法的局部视野瓶颈。在Game24、Six Fives和伦敦塔任务上，对六个前沿LLM进行的实验表明，BIGMAS能持续提升标准LLMs和LRMs的推理性能，优于包括ReAct和思维树在内的现有多智能体基线，这表明多智能体架构设计提供了与模型级推理增强正交的互补性收益。