# TimeCapsule: Generative Hallucination as a Method for Historical Sensemaking

arXiv: https://arxiv.org/abs/2607.24750 · HTML: https://mlsanity.com/paper/2607.24750/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 22 May 2026.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change](https://mlsanity.com/paper/1605.09096/) (2016)
- [Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Models](https://mlsanity.com/paper/2305.11483/) (2023)
- [Machine Learning Uncertainty as a Design Material: A Post-Phenomenological Inquiry](https://mlsanity.com/paper/2101.04035/) (2021)
- [Computational Hermeneutics: Evaluating generative AI as a cultural technology](https://mlsanity.com/paper/2604.16403/) (2026)
- [Thoughtful, Confused, or Untrustworthy: How Text Presentation Influences Perceptions of AI Writing Tools](https://mlsanity.com/paper/2504.20365/) (2025)

## Built on by (most cited first, 1 of 1)

- [L"ushi-1840: Building and Probing a Knowledge Cutoff for a Classical Chinese Language Model](https://mlsanity.com/paper/2610.16201/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) are temporally overexposed: trained on vast contemporary corpora, they encode present-day concepts that make them unreliable narrators of the past. We present TimeCapsule, a 1.2B-parameter LLaMA-style causal model trained exclusively on Victorian texts (1800-1875) as an epistemologically isolated generative archive. Quantitative evaluation shows a 45.4% perplexity reduction over a GPT-2 baseline on held-out Victorian prose, while larger contemporary causal models achieve lower raw perplexity through broader pretraining but lack temporal isolation. TimeCapsule exhibits computational sensemaking, generating historically plausible analogical explanations for unfamiliar modern concepts (e.g., describing a computer as a "hypertrophied lung"). A qualitative hermeneutic probe with two humanities scholars revealed a crisis of authenticity, as both misclassified approximately 40% of genuine Victorian excerpts as machine-produced. We argue that structural ignorance of the future transforms hallucinations into interpretive probes of nineteenth-century ontologies.