Large language models (LLMs) have achieved remarkable success across many domains, yet their generative capabilities have been misused to produce highly realistic fake news that threatens social stability. However, the main existing detection methods, including small language model (SLM)-based approaches and LLM-based detectors, exhibit substantial performance degradation on LLM-generated content, reflecting a lack of robustness. SLM-based detectors rely heavily on fixed data distributions and shallow textual cues, while LLM-based detectors depend on prompt-based inference, making them sensitive to prompting and vulnerable to LLM hallucinations. To address this challenge, we propose Fake News Court (FNC), a robust multi-agent adversarial framework that integrates the complementary strengths of LLM and SLM for detecting LLM-generated fake news. FNC adopts an adversarial learning paradigm in which a quality-controlled generator produces diverse and challenging fake news, while a hybrid detector combines multi-dimensional LLM-based agent reasoning with an SLM-based classifier to ensure stable decisions. Extensive experiments show that FNC achieves an overall accuracy improvement of approximately 6% over competitive baselines on LLM-generated fake news, confirming its robustness.

This paper studies the problem of text-attributed graph clustering, which aims to cluster each node into different groups using both textual attributes and structural information. Although graph neural networks (GNNs) have been proposed to solve this problem, their performance is usually limited when uncertain nodes are near the cluster boundaries due to label scarcity. In this paper, we introduce a new perspective of leveraging large language models (LLMs) to enhance text-attributed graph clustering and develop a novel approach named Multi-agent Collaboration with Ranking Guidance (MARK). The core of our MARK is to generate reliable guidance using the collaboration of three LLM-based agents as ranking-based supervision signals. In particular, we first conduct the coarse graph clustering, and utilize a concept agent to induce the semantics of each cluster. Then, we infer the robustness under perturbations to identify uncertain nodes and use a generation agent to produce synthetic text that closely aligns with their topology. An inference agent is adopted to provide the ranking semantics for each uncertain node in comparison to its synthetic counterpart. The consistent feedback between uncertain and synthetic texts is identified as reliable guidance for fine-tuning the clustering model within a ranking-based supervision objective. Experimental results on various benchmark datasets validate the effectiveness of the proposed MARK compared with competing baselines.