Misinformation on social media poses a critical threat to information credibility, as its diverse and context-dependent nature complicates detection. Large language model-empowered multi-agent systems (MAS) present a promising paradigm that enables cooperative reasoning and collective intelligence to combat this threat. However, conventional MAS suffer from an information-drowning problem, where abundant truthful content overwhelms sparse and weak deceptive cues. With full input access, agents tend to focus on dominant patterns, and inter-agent communication further amplifies this bias. To tackle this issue, we propose PAMAS, a multi-agent framework with perspective aggregation, which employs hierarchical, perspective-aware aggregation to highlight anomaly cues and alleviate information drowning. PAMAS organizes agents into three roles: Auditors, Coordinators, and a Decision-Maker. Auditors capture anomaly cues from specialized feature subsets; Coordinators aggregate their perspectives to enhance coverage while maintaining diversity; and the Decision-Maker, equipped with evolving memory and full contextual access, synthesizes all subordinate insights to produce the final judgment. Furthermore, to improve efficiency in multi-agent collaboration, PAMAS incorporates self-adaptive mechanisms for dynamic topology optimization and routing-based inference, enhancing both efficiency and scalability. Extensive experiments on multiple benchmark datasets demonstrate that PAMAS achieves superior accuracy and efficiency, offering a scalable and trustworthy way for misinformation detection.

翻译：社交媒体上的虚假信息因其多样性和情境依赖性而难以检测，对信息可信度构成严重威胁。基于大语言模型的多智能体系统（MAS）通过协同推理与集体智能提供了应对这一威胁的有效范式。然而，传统MAS存在信息淹没问题：大量真实内容会掩盖稀疏且微弱的欺骗性线索。在获取完整输入的情况下，智能体倾向于关注主导模式，而智能体间的通信会进一步放大这种偏差。为解决这一问题，我们提出了PAMAS——一种基于视角聚合的多智能体框架，它采用分层的、视角感知的聚合机制来突出异常线索并缓解信息淹没。PAMAS将智能体组织为三种角色：审计员、协调员和决策者。审计员从专业化的特征子集中捕获异常线索；协调员聚合这些视角以增强覆盖度并保持多样性；决策者则配备动态演化的记忆模块和完整上下文访问能力，综合所有下属的洞察以生成最终判断。此外，为提升多智能体协作效率，PAMAS引入了自适应机制，包括动态拓扑优化和基于路由的推理，从而同时提升效率与可扩展性。在多个基准数据集上的大量实验表明，PAMAS在准确性和效率方面均表现优异，为虚假信息检测提供了一种可扩展且可信赖的解决方案。