Towards the AI Historian: Agentic Information Extraction from Primary Sources
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914445475708928 |
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| author | Hufe, Lorenz Griesshaber, Niclas Greif, Gavin Eck, Sebastian Oliver Torr, Philip |
| author_facet | Hufe, Lorenz Griesshaber, Niclas Greif, Gavin Eck, Sebastian Oliver Torr, Philip |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_03553 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Towards the AI Historian: Agentic Information Extraction from Primary Sources Hufe, Lorenz Griesshaber, Niclas Greif, Gavin Eck, Sebastian Oliver Torr, Philip Artificial Intelligence Computation and Language Digital Libraries 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. |
| title | Towards the AI Historian: Agentic Information Extraction from Primary Sources |
| topic | Artificial Intelligence Computation and Language Digital Libraries |
| url | https://arxiv.org/abs/2604.03553 |