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通过进化进行推理：面向基于大语言模型的虚假新闻检测的自动元路径发现
该论文提出 MAGER，一个多智能体遗传进化框架，可自动发现针对 LLM 推理优化的元路径，将复杂传播图压缩为信息子图，以缓解信息过载和模态错配，使冻结的 LLM 能在零样本和少样本条件下进行结构感知的真实性推理。作者还引入图上下文学习策略，检索语义与结构相似的示例以增强分类与推理。摘要称在数据高效设置下显著提升冻结 LLM 作为独立虚假新闻检测器的表现，但未给出具体数据集与指标数值。
AI推荐理由 arXiv 新提交论文（2026-09-16），提出 MAGER 多智能体遗传进化框架，自动发现面向 LLM 推理的元路径，将传播图压缩为信息子图以缓解信息过载与模态错配，并引入图上下文学习检索相似示例。摘要给出方法动机与实验结论，但未提供具体数据集、指标数值或对比基线，效果幅度无法核实；代码链接存在但未验证可用性。对 LLM 结构感知推理与虚假新闻检测方向有参考价值。
72
正文
Computer Science > Artificial Intelligence
arXiv:2609.18597 (cs)
[Submitted on 16 Sep 2026]
Title:Reasoning through Evolution: Automatic Meta-path Discovery for LLM-based Fake News Detection
Authors:Ziyi Zhou, Xiaoming Zhang, Hui Pang, Yuting Zhang, Tiesunlong Shen, Bingyu Yan, Erik Cambria, Litian Zhang
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Abstract: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 this https URL.
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Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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arXiv:2609.18597 [cs.AI]
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arXiv:2609.18597v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.18597
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arXiv-issued DOI via DataCite (pending registration)
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From: Ziyi Zhou [view email]
[v1]
Wed, 16 Sep 2026 12:51:51 UTC (1,035 KB)
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