时间旅行引擎：共享的潜在时间流形使大型语言模型能够进行历史导航
Time Travel Engine: A Shared Latent Chronological Manifold Enables Historical Navigation in Large Language Models
- Peking University（北京大学）
- Zhejiang University（浙江大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
时间旅行引擎通过共享的潜在时间流形，使大型语言模型能够导航历史，揭示语言模型中时间进程的连续几何结构。
AI中文摘要：
时间作为人类认知的基本维度，然而大型语言模型（LLMs）如何编码时间进程的机制仍然不透明。我们证明其潜在空间中的时间信息并非以离散聚类的形式组织，而是以连续、可遍历的几何形式存在。我们引入了时间旅行引擎（TTE），一个以可解释性为导向的框架，将历时性语言模式投影到共享的时间流形上。不同于表面提示，TTE直接调节潜在表示，以诱导与目标时代一致的风格、词汇和概念变化。通过将历时性演变参数化为残差流中的连续流形，TTE能够在特定时期“zeitgeist”之间实现流畅导航，同时限制对未来的知识访问。此外，跨不同架构的实验揭示了中文和英语的时间子空间在拓扑上同构——表明不同语言共享一个普遍的历史演化的几何逻辑。这些发现将历史语言学与机械可解释性联系起来，为神经网络中的时间推理提供了一种新的范式。
英文摘要：
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.
