Large Language Models (LLMs) increasingly act as gateways to web content, shaping how millions of users encounter online information. Unlike traditional search engines, whose retrieval and ranking mechanisms are well studied, the selection processes of web-connected LLMs add layers of opacity to how answers are generated. By determining which news outlets users see, these systems can influence public opinion, reinforce echo chambers, and pose risks to civic discourse and public trust. This work extends two decades of research in algorithmic auditing to examine how LLMs function as news engines. We present the first audit comparing three leading agents, GPT-4o-Mini, Claude-3.7-Sonnet, and Gemini-2.0-Flash, against Google News, asking: \textit{How do LLMs differ from traditional aggregators in the diversity, ideology, and reliability of the media they expose to users?} Across 24 global topics, we find that, compared to Google News, LLMs surface significantly fewer unique outlets and allocate attention more unevenly. In the same way, GPT-4o-Mini emphasizes more factual and right-leaning sources; Claude-3.7-Sonnet favors institutional and civil-society domains and slightly amplifies right-leaning exposure; and Gemini-2.0-Flash exhibits a modest left-leaning tilt without significant changes in factuality. These patterns remain robust under prompt variations and alternative reliability benchmarks. Together, our findings show that LLMs already enact \textit{agentic editorial policies}, curating information in ways that diverge from conventional aggregators. Understanding and governing their emerging editorial power will be critical for ensuring transparency, pluralism, and trust in digital information ecosystems.

翻译：大型语言模型（LLMs）日益成为网络内容的门户，塑造着数百万用户在线获取信息的方式。与传统搜索引擎不同，后者的检索和排序机制已得到深入研究，而联网LLM的选择过程为其答案生成增添了更多不透明性。通过决定用户可见的新闻来源，这些系统能够影响公众舆论、强化信息茧房，并对公民话语和公众信任构成风险。本研究延续了算法审计领域二十年的研究传统，旨在检验LLM作为新闻引擎的功能。我们首次进行对比审计，将三种领先的智能体——GPT-4o-Mini、Claude-3.7-Sonnet和Gemini-2.0-Flash——与谷歌新闻进行比较，探究以下问题：*LLM与传统聚合器在向用户曝光的媒体多样性、意识形态倾向和可靠性方面有何不同？* 在对24个全球主题的分析中，我们发现，与谷歌新闻相比，LLM呈现的独特新闻来源显著更少，且注意力分配更不均衡。同样地，GPT-4o-Mini更强调事实性较强且偏右翼的新闻源；Claude-3.7-Sonnet偏向机构及公民社会领域，并轻微放大了右翼倾向的曝光；而Gemini-2.0-Flash则表现出轻微的左翼倾斜，在事实性方面无显著变化。这些模式在提示词变化及替代可靠性基准下依然稳健。综上所述，我们的发现表明，LLM已实施*能动编辑策略*，以不同于传统聚合器的方式筛选信息。理解并监管这些新兴的编辑权力，对于确保数字信息生态系统中的透明度、多元化和信任至关重要。