TimeCapsule: Generative Hallucination as a Method for Historical Sensemaking
A new AI model trained exclusively on Victorian-era texts demonstrates how generative hallucinations can serve as interpretive tools for understanding historical ontologies, achieving a 45.4% perplexity reduction over GPT-2 on historical prose while revealing a crisis of authenticity in machine-generated historical content.
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[Submitted on 22 May 2026]
Title:TimeCapsule: Generative Hallucination as a Method for Historical Sensemaking
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Abstract: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.
Comments: 10 pages, 4 figures. Accepted to Creativity and Cognition (C&C '26)
Subjects:
Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
Cite as: arXiv:2607.24750 [cs.CL]
(or arXiv:2607.24750v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.24750
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1145/3803784.3807554
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Submission history
From: Hamed Yaghoobian [view email] [v1] Fri, 22 May 2026 18:25:04 UTC (662 KB)
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