Reasoning through Evolution: Automatic Meta-path Discovery for LLM-based Fake News Detection· 通过进化推理：自动元路径发现
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, enabl
通过进化算法自动发现元路径，提升大模型假新闻检测能力。
- 核心方法
- 提出MAGER多代理遗传进化框架，自动发现适合大模型推理的元路径，将复杂传播图压缩为信息丰富的子图。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有假新闻检测方法依赖大量标注数据且泛化能力差，大模型处理原始传播图时存在模态不匹配和信息过载问题。
- 关键实验
- 未提供
- 主要贡献
- 实现了自动元路径发现，提高了大模型在零样本和少样本设置下的结构感知推理能力。
- 意义与局限
- 该方法有望减少假新闻检测对标注数据的依赖，提高模型的泛化能力，但目前缺乏实验验证。