# AI Historian: Helping historians organize and verify person-centred temporal clues from dispersed historical narratives

arXiv: https://arxiv.org/abs/2608.29133 · HTML: https://mlsanity.com/paper/2608.29133/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Builds on (most cited first, 16 of 16)

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- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
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- [EventKG - the Hub of Event Knowledge on the Web - and Biographical Timeline Generation](https://mlsanity.com/paper/1905.08794/) (2019)
- [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021)
- [Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE](https://mlsanity.com/paper/2310.09550/) (2023)
- [AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models](https://mlsanity.com/paper/2403.06574/) (2024)
- [Temporal Information Extraction by Predicting Relative Time-lines](https://mlsanity.com/paper/1808.09401/) (2018)

## Abstract (arXiv)

History is not preserved in complete, continuous form. Accounts of a person's activities, relationships and historical contexts are scattered across texts, chapters and narrative perspectives; historians must retrieve, identify and compare these materials to reconstruct temporal sequences and verify them against sources. Here we present AI Historian (AIH), an AI agent system that helps historians organize person-time evidence from dispersed biographical narratives. It takes source sentences as evidence units, identifies people and temporal cues, verifies candidate cross-text associations and infers comparable temporal ranges while preserving traceable source-text evidence. We evaluated AIH on six Shiji cases concerning Liu Bang, Xiang Yu and Xiao He. AIH Agent achieved a temporal-localization MicroIoU of 86.2%, compared with 81.3% for human-only annotation and 17.1% for direct large-language-model prompting; it required about 14 min, versus 1 h 32 min for human-only annotation. We further applied AIH to the Twenty-Four Histories and other ancient Chinese histories, ancient Japanese and Korean histories, and modern and contemporary historical materials, and released the results through Westlake Historian. These results indicate that AIH can reduce the cost of organizing historical materials at scale while turning connections obscured by chapter-based narration into traceable, revisable research questions for collaborative testing.