Although large language models (LLMs) have revolutionized natural language processing capabilities, their practical implementation as autonomous multi-agent systems (MAS) for industrial problem-solving encounters persistent barriers. Conventional MAS architectures are fundamentally restricted by inflexible, hand-crafted graph topologies that lack contextual responsiveness, resulting in diminished efficacy across varied academic and commercial workloads. To surmount these constraints, we introduce AMAS, a paradigm-shifting framework that redefines LLM-based MAS through a novel dynamic graph designer. This component autonomously identifies task-specific optimal graph configurations via lightweight LLM adaptation, eliminating the reliance on monolithic, universally applied structural templates. Instead, AMAS exploits the intrinsic properties of individual inputs to intelligently direct query trajectories through task-optimized agent pathways. Rigorous validation across question answering, mathematical deduction, and code generation benchmarks confirms that AMAS systematically exceeds state-of-the-art single-agent and multi-agent approaches across diverse LLM architectures. Our investigation establishes that context-sensitive structural adaptability constitutes a foundational requirement for high-performance LLM MAS deployments.

翻译：尽管大语言模型（LLMs）已彻底革新了自然语言处理能力，但将其作为自主多智能体系统（MAS）应用于工业问题解决时，仍面临持续存在的障碍。传统的MAS架构从根本上受到缺乏上下文响应能力的僵化、人工设计的图拓扑结构的限制，导致其在多样化的学术和商业工作负载中效能降低。为克服这些限制，我们提出了AMAS——一种通过新型动态图设计器重新定义基于LLM的MAS的范式转换框架。该组件通过轻量级LLM自适应，自主识别任务特定的最优图配置，从而消除了对单一、普遍应用的结构模板的依赖。相反，AMAS利用个体输入的固有属性，通过任务优化的智能体路径智能引导查询轨迹。在问答、数学推理和代码生成基准测试上的严格验证证实，AMAS在不同LLM架构上系统地超越了最先进的单智能体与多智能体方法。我们的研究表明，上下文敏感的结构自适应是实现高性能LLM MAS部署的基础性要求。