PAMAS: Self-Adaptive Multi-Agent System with Perspective Aggregation for Misinformation Detection

2026-02-03

AI Summary

This work addresses the challenges posed by the high diversity and context dependence of misinformation on social media, which often overwhelms conventional multi-agent systems and obscures subtle deceptive cues. To overcome these limitations, the authors propose an adaptive multi-agent framework featuring a tripartite role structure—auditor, coordinator, and decision-maker—augmented with a perspective-aware hierarchical aggregation mechanism and an adaptive topology optimization strategy. This design effectively amplifies anomalous signals while integrating heterogeneous viewpoints. Leveraging large language model–driven collaborative reasoning, dynamic routing, and an evolving memory mechanism, the approach significantly outperforms state-of-the-art methods across multiple benchmark datasets, achieving superior detection accuracy and reasoning efficiency. The proposed solution offers a scalable and robust paradigm for misinformation detection in complex social media environments.

Technology Category

• Multiagent Systems: Adversarial Agents
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Responsible Web: Mitigating misinformation and disinformation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• misinformation detection
• multi-agent system
• information drowning
• perspective aggregation
• social media

Innovation

• multi-agent system
• perspective aggregation
• misinformation detection
• self-adaptive mechanism
• anomaly cue highlighting