Wikipedia is a critical resource for modern NLP, serving as a rich repository of up-to-date and citation-backed information on a wide variety of subjects. The reliability of Wikipedia—its groundedness in its cited sources—is vital to this purpose. This work analyzes both how grounded Wikipedia is and how readily fine-grained grounding evidence can be retrieved. To this end, we introduce PeopleProfiles—a large-scale, multi-level dataset of claim support annotations on biographical Wikipedia articles. We show that: ~22% of claims in Wikipedia *lead* sections are unsupported by the article body; ~30% of annotated claims in the article *body* are unsupported by their (publicly accessible) sources; and real-world Wikipedia citation practices often differ from documented standards. Finally, we show that complex evidence retrieval remains a challenge—even for recent reasoning rerankers.

We introduce the task of grounded article generation with the goal of creating a Wikipedia-style article from multiple diverse videos about real-world events—from natural disasters to political elections—where all the information in the article is supported by video evidence. Videos are intuitive sources for retrieval-augmented generation (RAG), but most contemporary RAG workflows focus heavily on text while existing methods for video-based summarization focus on low-level scene understanding rather than high-level event semantics. To close this gap, we introduce , a benchmark consisting of expert-written articles and densely annotated videos that provide evidence for articles’ claims, facilitating the integration of video into RAG pipelines and enabling the creation of in-depth content that is grounded in multimodal sources. We further propose Collaborative Article Generation (CAG), a novel interactive method for article creation from multiple videos. CAG leverages an iterative interaction between an r1-style reasoning model and a VideoLLM to draw higher-level inferences about the target event than is possible with VideoLLMs alone, which fixate on low-level visual features. We benchmark state-of-the-art VideoLLMs and CAG in both oracle retrieval and RAG settings and find that CAG consistently outperforms alternative methods, while suggesting intriguing avenues for future work.