Recent advances in large language models (LLMs) have enabled the large-scale generation of highly fluent and deceptive news-like content. While prior work has often treated fake news detection as a binary classification problem, modern fake news increasingly arises through human-AI collaboration, where strategic inaccuracies are embedded within otherwise accurate and credible narratives. These mixed-truth cases represent a realistic and consequential threat, yet they remain underrepresented in existing benchmarks. To address this gap, we introduce MANYFAKE, a synthetic benchmark containing 6,798 fake news articles generated through multiple strategy-driven prompting pipelines that capture many ways fake news can be constructed and refined. Using this benchmark, we evaluate a range of state-of-the-art fake news detectors. Our results show that even advanced reasoning-enabled models approach saturation on fully fabricated stories, but remain brittle when falsehoods are subtle, optimized, and interwoven with accurate information.

翻译：大型语言模型（LLMs）的最新进展使得大规模生成流畅且具有欺骗性的新闻类内容成为可能。虽然以往研究常将假新闻检测视为二分类问题，但现代假新闻越来越多地通过人机协作产生，其中战略性不准确信息被嵌入到原本准确可信的叙述中。这类混合真伪案例代表着现实且影响深远的威胁，然而在现有基准测试中却鲜有体现。为弥补这一空白，我们提出MANYFAKE——一个包含6,798篇假新闻文章的综合基准测试，这些文章通过多种策略驱动的提示流水线生成，涵盖了假新闻构建与优化的多种途径。利用该基准，我们评估了一系列最先进的假新闻检测器。结果表明，即便具有高级推理能力的模型在面对完全捏造的故事时接近饱和，但当虚假信息变得微妙、经过优化并与准确信息交织在一起时，这些模型仍然脆弱。