AI助手帮史学家从分散文本中整理人物时间线索，提升效率与可验证性。
AI Historian: Helping historians organize and verify person-centred temporal clues from dispersed historical narratives
- 构建AI代理系统，自动识别人物与时间线索并验证跨文本关联
- 在六例《史记》案例中，时间定位准确率达86.2%，远超人工的81.3%和直接大模型提示的17.1%
- 适用于古籍、跨国历史资料，支持协作式研究与溯源验证
历史记载并非完整连续。人物活动、关系及历史背景信息散落于不同文本、章节与叙述视角中，史学家需检索、识别并比对这些材料以重构时间序列并验证来源。本文提出AI Historian（AIH），一个帮助史学家从分散的传记叙事中组织人物-时间证据的AI代理系统。它以源句为证据单元，识别人物与时间线索，验证候选跨文本关联，并推断可比的时间范围，同时保留可追溯的原文依据。我们在六例涉及刘邦、项羽、萧何的《史记》案例上评估了AIH，其时间定位的MicroIoU达86.2%，高于人类仅标注的81.3%和直接大语言模型提示的17.1%；耗时约14分钟，远低于人类标注的1小时32分钟。我们进一步将AIH应用于《二十四史》及其他古代中日韩历史文献以及近现代史料，并通过Westlake Historian发布结果。结果表明，AIH可在大规模下降低史料组织成本，将章节结构掩盖的关联转化为可追溯、可修订的合作研究问题。
原文摘要 · Abstract (English)
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.
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