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arXiv AI By Ziyi Zhou, Xiaoming Zhang, Hui Pang, Yuting Zhang, Tiesunlong Shen, Bingyu Yan, Erik Cambria, Litian Zhang

Reasoning through Evolution: Automatic Meta-path Discovery for LLM-based Fake News Detection

Read the original on arXiv AI →

The paper introduces MAGER, a multi-agent genetic evolution framework that automatically discovers meta-paths for large language models (LLMs) to reason about fake news propagation graphs. By compressing complex propagation structures into informative subgraphs, MAGER reduces modality mismatch and information overload, enabling frozen LLMs to perform structure-aware veracity reasoning. The authors also propose a graph in-context learning strategy that retrieves semantically and structurally similar demonstrations to enhance classification and reasoning, and report that MAGER significantly improves LLM performance in data‑efficient settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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