Epstein Files Engine: Agentic Search for Investigative Journalism

Abstract:On Jan. 30, 2026, the U.S. Department of Justice released a mixed-media collection concerning Jeffrey Epstein, including about three million pages of PDFs. We describe the Epstein Files Engine, an A.I. agent The New York Times deployed to investigate the files. The Engine translated reporter questions into Google BigQuery SQL queries across three corpora: Epstein-related releases, the Times's archive and external, Epstein-related news headlines. It used an LLM to plan queries and returned citation-rich answers a reporter could verify and trust. More than 100 journalists used the Engine, and it contributed to at least 20 published stories. We report how reporters queried it and describe Diff, our text-and-visual duplicate matching method that amplified novelty signals and allowed the Engine to surface genuinely new information. We argue that newsroom agents serve newsrooms best not as autonomous writers, but as interfaces to source material and institutional knowledge.

6 pages, 2 figures, 2 tables. Presented at the Computation + Journalism Symposium (C+J 2026)

Human-Computer Interaction (cs.HC); Computation and Language (cs.CL); Computers and Society (cs.CY); Information Retrieval (cs.IR)

arXiv:2609.30611 [cs.HC]

(or arXiv:2609.30611v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2609.30611 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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