让大模型穿越时间，按历史风格生成文本
Time Travel Engine: A Shared Latent Chronological Manifold Enables Historical Navigation in Large Language Models
- 在模型隐空间构建连续时间几何结构，实现历史风格迁移
- 可精准导航不同历史时期的语言风格，且不泄露未来信息
- 中英文模型共享相同时间拓扑，揭示语言演化的普适规律
时间是人类认知的基本维度，但大语言模型如何编码时间演进仍不清晰。我们发现，模型隐空间中的时间信息并非离散簇状分布，而是一个连续可遍历的几何结构。提出时间旅行引擎（TTE），一种基于可解释性的框架，将历时性语言模式投影到共享的时间流形上。与表面提示不同，TTE直接调控隐表示，引发与目标时代一致的风格、词汇和概念转变。通过将历时演化参数化为残差流中的连续流形，TTE实现对时期性“时代精神”的流畅导航，同时限制未来知识访问。跨多种架构的实验表明，中英文模型的时间子空间具有拓扑同构性——说明不同语言共享历史演化的普遍几何逻辑。该研究连接历史语言学与机制可解释性，为神经网络中的时间推理控制提供新范式。
原文摘要 · Abstract (English)
Time functions as a fundamental dimension of human cognition, yet the mechanisms by which Large Language Models (LLMs) encode chronological progression remain opaque. We demonstrate that temporal information in their latent space is organized not as discrete clusters but as a continuous, traversable geometry. We introduce the Time Travel Engine (TTE), an interpretability-driven framework that projects diachronic linguistic patterns onto a shared chronological manifold. Unlike surface-level prompting, TTE directly modulates latent representations to induce coherent stylistic, lexical, and conceptual shifts aligned with target eras. By parameterizing diachronic evolution as a continuous manifold within the residual stream, TTE enables fluid navigation through period-specific "zeitgeists" while restricting access to future knowledge. Furthermore, experiments across diverse architectures reveal topological isomorphism between the temporal subspaces of Chinese and English-indicating that distinct languages share a universal geometric logic of historical evolution. These findings bridge historical linguistics with mechanistic interpretability, offering a novel paradigm for controlling temporal reasoning in neural networks.
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