The paper introduces Data Journalist Agent (Data2Story), a multi‑agent framework that orchestrates specialized roles into a single virtual newsroom to produce evidence‑grounded, multimodal news stories. It ensures every claim is traceable to data, code, or external references via an Inspector, and generates interactive visualizations such as maps and audio to match reader interests. Evaluations on 18 articles show competitive performance in angle coverage, rubric scores, and verifiability, while human writers still lead in editorial angle and creative design.
By Kevin Qinghong Lin, Batu EI, Yuhong Shi, Pan Lu, Juil Sock, Djordje Padejski, Philip Torr, James Zou
The Epstein Files Engine is an AI agent developed by the New York Times to help journalists investigate a massive mixed‑media collection released by the U.S. Department of Justice on January 30, 2026, which contains about three million pages of PDFs related to Jeffrey Epstein. The Engine translates reporter questions into Google BigQuery SQL queries across three corpora—Epstein‑related releases, the Times’s archive, and external Epstein‑related news headlines—using an LLM to plan queries and return citation‑rich answers that reporters can verify. Over 100 journalists used the Engine, contributing to at least 20 published stories, and the system includes a Diff method for text‑and‑visual duplicate matching to surface genuinely new information.
By Duy K. Nguyen, Teresa Mondr\'ia Terol, Dylan Freedman, Zach Seward
arXiv:2509. 00446v2 Announce Type: replace Abstract: Recent advances in autonomous digital agents from industry (e.
By Yen-Che Chien, Kuang-Da Wang, Wei-Yao Wang, Wen-Chih Peng
arXiv:2610.35793v1 Announce Type: new
Abstract: Deep research, or any use of large language models (LLMs) to read a large document collection and produce insights, requires synthesizing information f...
By Tiasa Singha Roy, Manya Wadhwa, Greg Durrett
arXiv:2605. 21347v3 Announce Type: replace Abstract: Diagnosing failures in LLM agents remains largely manual.
By Akshay Manglik, Apaar Shanker, Kaustubh Deshpande, Jason Qin, Yash Maurya, Veronica Chatrath, Vijay S. Kalmath, Levi Lentz, Yuan Xue
Same Story, Different Lens is an experimental prototype that compares how 17 news outlets cover the same events by collecting world and politics coverage, grouping reports about identical incidents, and generating structured analyses of sentiment, political lean, framing, loaded language, omissions, and estimated proportions of factual claims versus opinion. The system employs three model roles—article analysis, source support assessment, and cross‑outlet briefing generation—to produce JSONL archives of its findings. Evaluation includes comparing outlet‑level lean estimates with predefined reference ratings and summarizing the Critic’s grounding scores, which indicate the model’s assessment of support within the supplied text.
By Affan Yasin, Yueqi Lyu, Rubia Fatima, Weinchen Zhong, Keyi Li
arXiv:2608. 14446v1 Announce Type: new Abstract: In the current artificial intelligence-driven innovation era, the pace of knowledge growth is accelerating, and is hard to keep up with.
By Beatrice Alessandra Motetti, Emilien Guandalino, Daniele Jahier Pagliari, Alessio Burrello, Lorenz K. M\"uller, Konstantin Berestizshevsky, Lukas Cavigelli
The paper introduces ChronoFake, a benchmark of 30,706 news articles that isolates temporally disordered fake news, where factual events are rearranged into false chronologies. It identifies the "evidence coherence trap," where verification relies on locally coherent but temporally mismatched evidence. To counter this, the authors propose DelSA, a document-selection agent that iteratively refines evidence during training and uses a frozen rulebook for inference, leveraging an LLM for evidence characterization and a verifier-trained reranker for timeline consistency, achieving superior performance on ChronoFake and competitive results on other datasets.
By Chi Wang, Min Gao, Yifei Zhang, Bincheng Gu, Zongwei Wang, Wentao Li
The paper introduces Video‑Centered Deep Research Report Generation, a new benchmark that evaluates agents on their ability to generate long, interactive research reports from a source video and user request. It presents the OmniDR‑Bench dataset with 50 tasks in Chinese and English, 19 subcategories, and 6,414 criteria, and proposes Omni Atomic Rubric Trees (Omni‑ARTs) for evaluation across audiovisual grounding, multimodal evidence use, and interactive presentation. The authors also detail a skill‑based workflow, an automated rubric construction process, and report that current agents achieve a best score of 63.3/100, highlighting gaps in content coverage, causal analysis, and citation accuracy.
By Jiahao Meng, Qize Yang, Yuxuan Wang, Yunfei Chu, Wensi Dai, Jin Xu, Yunhai Tong
arXiv:2607. 17291v1 Announce Type: new Abstract: Deep research agents increasingly operate over the open web, where relevant records coexist with redundant summaries, outdated reports, and misleading documents.
By Jun Nie, Zhiqin Yang, Zhenheng Tang, Yonggang Zhang, Xiaowen Chu, Xinmei Tian, Bo Han
arXiv:2610.33240v1 Announce Type: new
Abstract: As frontier AI systems increasingly serve as sources of political and current-events information, evaluating the epistemic quality of their responses i...
By Matt Wilde, Jillian Fisher, Andrew B. Hall, Kathryn Salam, Robbie Goldfarb
arXiv:2606. 19749v1 Announce Type: new Abstract: A new class of agentic review systems are emerging as a remedy to the pressure placed on peer review systems by AI-assisted research, but it is unclear how they should be evaluated.
By Dang Nguyen, Wanqing Hao, Yanai Elazar, Chenhao Tan