Readers of online news often lack the time and domain expertise required to verify unfamiliar claims and sources. Professional fact-checkers address this gap through lateral reading, an iterative workflow of asking investigative questions, searching for external evidence, and synthesizing findings with attribution. We present an iterative multi-agent Retrieval-Augmented Generation (RAG) system that operationalizes this workflow for the TREC 2025 DRAGUN Track. Given a news article, specialized agents (1) generate investigative queries, (2) retrieve and filter evidence from the MS MARCO V2.1 Segmented Corpus using a three-stage retriever (BM25+RM3, cross-encoder reranking, and LLM-based selection), and (3) apply an information-sufficiency evaluator that decides whether additional searching is required before writing. The final report generator produces a 250-word trustworthiness report grounded in retrieved segments, guided by automatically generated critical investigative questions. On the official DRAGUN rubric-based evaluation with 30 news articles, our system using GPT-4.1 ranked first on report generation quality, achieving the highest mean supportive score (0.230) with low contradiction (0.013).

Many readers today struggle to assess the trustworthiness of online news because reliable reporting coexists with misinformation. The TREC 2025 DRAGUN (Detection, Retrieval, and Augmented Generation for Understanding News) Track provided a venue for researchers to develop and evaluate assistive RAG systems that support readers' news trustworthiness assessment by producing reader-oriented, well-attributed reports. As the organizers of the DRAGUN track, we describe the resources that we have newly developed to allow for the reuse of the track's tasks. The track had two tasks: (Task 1) Question Generation, producing 10 ranked investigative questions; and (Task 2, the main task) Report Generation, producing a 250-word report grounded in the MS MARCO V2.1 Segmented Corpus. As part of the track's evaluation, we had TREC assessors create importance-weighted rubrics of questions with expected short answers for 30 different news articles. These rubrics represent the information that assessors believe is important for readers to assess an article's trustworthiness. The assessors then used their rubrics to manually judge the participating teams' submitted runs. To make these tasks and their rubrics reusable, we have created an automated process to judge runs not part of the original assessing. We show that our AutoJudge ranks existing runs well compared to the TREC human-assessed evaluation (Kendall's τ = 0.678 for Task 1 and τ = 0.872 for Task 2). These resources enable both the evaluation of RAG systems for assistive news trustworthiness assessment and, with the human evaluation as a benchmark, research on improving automated RAG evaluation.

When searching the web for answers to health questions, people can make incorrect decisions that have a negative effect on their lives if the search results contain misinformation. To reduce health misinformation in search results, we need to be able to detect documents with correct answers and promote them over documents containing misinformation. Determining the correct answer has been a difficult hurdle to overcome for participants in the TREC Health Misinformation Track. In the 2021 track, automatic runs were not allowed to use the known answer to a topic's health question, and as a result, the top automatic run had a compatibility-difference score of 0.043 while the top manual run, which used the known answer, had a score of 0.259. The compatibility-difference measures the ability of methods to rank correct and credible documents before incorrect and non-credible documents. By using an existing set of health questions and their known answers, we show it is possible to learn which web hosts are trustworthy, from which we can predict the correct answers to the 2021 health questions with an accuracy of 76%. Using our predicted answers, we can promote documents that we predict contain this answer and achieve a compatibility-difference score of 0.129, which is a three-fold increase in performance over the best previous automatic method.