# Using Generative Agents to Create Tip Sheets for Investigative Data Reporting

arXiv: https://arxiv.org/abs/2409.07286 · HTML: https://mlsanity.com/paper/2409.07286/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 3 of 3)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Is GPT-4 a Good Data Analyst?](https://mlsanity.com/paper/2305.15038/) (2023)
- [Understanding Practices around Computational News Discovery Tools in the Domain of Science Journalism](https://mlsanity.com/paper/2311.06864/) (2023)

## Built on by (most cited first, 1 of 1)

- [Epstein Files Engine: Agentic Search for Investigative Journalism](https://mlsanity.com/paper/2609.30611/) (2026)

## Abstract (arXiv)

This paper introduces a system using generative AI agents to create tip sheets for investigative data reporting. Our system employs three specialized agents--an analyst, a reporter, and an editor--to collaboratively generate and refine tips from datasets. We validate this approach using real-world investigative stories, demonstrating that our agent-based system generally generates more newsworthy and accurate insights compared to a baseline model without agents, although some variability was noted between different stories. Our findings highlight the potential of generative AI to provide leads for investigative data reporting.