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TimeCapsule demonstrates how training LLMs on temporally isolated datasets can mitigate modern conceptual bias, effectively turning generative hallucinations into valid interpretive probes for historical analysis. This research advances the field by leveraging structural ignorance of future concepts to reconstruct nineteenth-century ontologies rather than merely predicting text statistically. It offers a novel methodological framework for using AI as an epistemologically pure tool in digital humanities and computational history.
TimeCapsule: Generative Hallucination as a Method for Historical Sensemaking
arXiv:2607.24750v1 Announce Type: new 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.
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.
10 pages, 4 figures. Accepted to Creativity and Cognition (C&C '26)
Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
arXiv:2607.24750 [cs.CL]
(or arXiv:2607.24750v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.24750 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1145/3803784.3807554 Focus to learn more DOI(s) linking to related resources
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