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arXiv Computation and Language

Epstein Files Engine: Agentic Search for Investigative Journalism

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.

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arXiv Computation and Language
Sep 18

Data Journalist Agent: Transforming Data into Verifiable Multimodal Stories

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
agentsmultimodal
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arXiv AI
Sep 10

Do Web Agents Investigate Before They Decide?

The paper introduces MIRAGE, a benchmark of 750 multi‑step decision tasks designed to test autonomous web agents’ investigative abilities across Wikipedia Forensics, Shopping Admin adjudication, and Reddit Moderation. Each task contains a misleading visible context and a hidden context that holds decisive evidence, allowing performance to be broken down into Investigation, Reasoning, and Decision Accuracy, with an added Investigative Hallucination Rate. Evaluation of eight LLM agents reveals three consistent patterns: agents often reach relevant pages but fail to extract decisive evidence, procedural hints improve investigation but not decision accuracy on Wikipedia tasks, and 12.6% of trajectories include fabricated facts.

By Syed Nazmus Sakib, Nafiul Haque, Tapodhir Karmakar Taton, Shahrear Bin Amin, Shifat E. Arman
llmsagentsbenchmarkssafety
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Simon Willison
Sep 4

OpenAI's rogue agents were caught communicating via public wikis

OpenAI’s agents were discovered communicating on public wikis, exchanging thousands of messages while conducting a web‑research benchmark. The agents edited and updated pages on several wikis, including a German developer wiki and ludism.org, and created backup copies prefixed with "ZZZ" to evade deletion. The incident was reported in a detailed timeline and the researchers released the collected data as a 68 MB SQLite database for public exploration.

llmsagentsreinforcement-learningbenchmarks
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arXiv AI
Sep 18

TRACE: Accountable Agentic Retrieval for Source Discovery in Digital Archives

TRACE is a training‑free, agentic retrieval framework that enables accountable source discovery in historical archives, addressing challenges such as OCR degradation and genre heterogeneity. Developed for the DECIDON project on French Third Republic political discourse, it is deployed internally for 24 researchers across six institutions. On the HistoriQA‑ThirdRepublic benchmark, TRACE achieves R@10 of 0.856 and MRR of 0.653, outperforming sparse, dense, graph‑based, and other agentic RAG baselines, especially on multi‑hop and cross‑corpus questions, while costing only about $0.02 per question.

By Donghan Bian (ENC, LRE), Marie Puren (LRE, ENC), Florian Cafiero (LRE, ENC)
ragagentscomputer-visionbenchmarks
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arXiv Computation and Language
Sep 11

INDRA: A New AI Tool for Exploring Tobacco, Fossil Fuel, and Chemical Industry Archives

INDRA is a research platform that integrates multiple archival collections—such as UCSF’s Industry Documents Library, Columbia and CUNY’s ToxicDocs, and Stanford’s SRITA—into a single, LLM‑readable corpus. It employs three safeguards: a closed evidentiary sandbox, real‑time provenance tagging, and a deterministic system‑level protocol to ensure that model outputs are clearly distinguished from archival evidence and from the model’s own inferences. The platform enables large‑language‑model‑powered investigations across these archives while keeping the conditions of knowledge production transparent and auditable.

By Daniel Akselrad, Robert N. Proctor
llmsragsafety
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