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The paper introduces TimeCapsule, a 1.2B-parameter LLM trained exclusively on Victorian texts (1800-1875) to generate historically plausible analogies for modern concepts, achieving a 45.4% perplexity reduction over GPT-2 on Victorian prose while exhibiting computational sensemaking through structured ignorance of the future.