Towards the AI Historian: Agentic Information Extraction from Primary Sources

2026-04-03

AI Summary

Historical research lacks AI tools tailored to its needs, making it difficult to efficiently extract structured data from raw document images. To address this gap, this work introduces the first module of the Chronos system, pioneering an agent-driven, interactive, and customizable paradigm for information extraction that overcomes the limitations of conventional vision-language models (VLMs) with rigid, fixed pipelines. By integrating VLMs with natural language–based interactive agents, the approach enables historians to flexibly design, evaluate, and iteratively refine information extraction workflows for heterogeneous historical documents through intuitive natural language commands. The core module has been open-sourced, empowering researchers to maintain full control over their AI-assisted datafication processes.

Abstract

AI is supporting, accelerating, and automating scientific discovery across a diverse set of fields. However, AI adoption in historical research remains limited due to the lack of solutions designed for historians. In this technical progress report, we introduce the first module of Chronos, an AI Historian under development. This module enables historians to convert image scans of primary sources into data through natural-language interactions. Rather than imposing a fixed extraction pipeline powered by a vision-language model (VLM), it allows historians to adapt workflows for heterogeneous source corpora, evaluate the performance of AI models on specific tasks, and iteratively refine workflows through natural-language interaction with the Chronos agent. The module is open-source and ready to be used by historical researchers on their own sources.

Problem

• AI Historian
• Information Extraction
• Primary Sources
• Historical Research
• Vision-Language Model

Innovation

• agentic AI
• information extraction
• primary sources
• vision-language model
• human-AI interaction