Towards the AI Historian: Agentic Information Extraction from Primary Sources

Computer Science > Artificial Intelligence [Submitted on 4 Apr 2026] Towards the AI Historian: Agentic Information Extraction from Primary Sources Lorenz Hufe, Niclas Griesshaber, Gavin Greif, Sebastian Oliver Eck, Philip Torr 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. Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Digital Libraries (cs.DL) Cite as: arXiv:2604.03553 [cs.AI]   (or arXiv:2604.03553v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.03553 Focus to learn more Submission history From: Niclas Griesshaber [view email] [v1] Sat, 4 Apr 2026 02:38:23 UTC (2,962 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CL cs.DL References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)