# Epstein Files Engine: Agentic Search for Investigative Journalism

arXiv: https://arxiv.org/abs/2609.30611 · HTML: https://mlsanity.com/paper/2609.30611/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 6 of 6)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [ImageBind: One Embedding Space To Bind Them All](https://mlsanity.com/paper/2305.05665/) (2023)
- [Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows](https://mlsanity.com/paper/2411.07763/) (2024)
- [Using Generative Agents to Create Tip Sheets for Investigative Data Reporting](https://mlsanity.com/paper/2409.07286/) (2024)
- [On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search](https://mlsanity.com/paper/2509.25494/) (2025)
- [EntSQL: A Benchmark for Grounding Text-to-SQL in Long-Context Enterprise Knowledge](https://mlsanity.com/paper/2606.03363/) (2026)

## Abstract (arXiv)

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.