提出自动发现元路径的 MAGER 框架用于基于 LLM 的假新闻检测
Reasoning through Evolution: Automatic Meta-path Discovery for LLM-based Fake News Detection
这个方法挺有意思，用进化框架自动发现元路径，让 LLM 能更高效地处理传播结构，解决信息过载问题，对假新闻检测很有帮助。
现有基于 GNN 的假新闻检测方法需要大量标注数据且泛化能力有限。本文提出 MAGER 框架，通过多智能体遗传进化自动发现元路径，将复杂传播图压缩为更简洁的子图，解决 LLM 直接处理原始图结构时的信息过载和模态不匹配问题，使冻结的 LLM 能够在零样本和少样本设置下进行结构感知的可信度推理。实验表明，MAGER 显著提升了冻结 LLM 作为独立检测器的数据效率。
Reasoning through Evolution: Automatic Meta-path Discovery for LLM-based Fake News Detection
Propagation structures provide crucial evidence for fake news detection, yet existing approaches primarily rely on supervised GNN-based models, which require substantial labeled data and exhibit limited generalization. Although large language models (LLMs) exhibit strong reasoning capabilities, directly feeding them raw propagation graphs creates a significant modality mismatch and severe information overload, making structure-aware reasoning unreliable in zero-shot and few-shot settings. To bridge this gap, we propose MAGER, a multi-agent genetic evolution framework that automatically discovers meta-paths optimized for LLM reasoning. By compressing complex propagation graphs into informative subgraphs, the evolved meta-paths alleviate both information overload and modality mismatch, enabling frozen LLMs to perform structure-aware veracity reasoning. We further introduce a graph in-context learning strategy that retrieves semantically and structurally similar demonstrations to strengthen classification and reasoning. Extensive experiments show that MAGER substantially improves frozen LLMs as standalone fake news detectors in data-efficient settings. Our code is available at https://github.com/SenticNet/MAGER.