DeepSeek-R1的横空出世标志着人工智能研究范式的历史性转折。这一突破性成果颠覆了传统AI系统依赖概率生成与模式匹配的圭臬，通过思维链推理与强化学习的深度融合，首次实现了工业级的人工智能推理能力。其创新性的推理架构不 仅极大地降低了训练专业人工智能模型的成本，更在数学证明、逻辑推理等复杂任务中展现出类人的思维链特征，推动了从 “概率生成”到“因果推理”的深刻变革，开启了AI技术发展的新纪元。本文对DeepSeek系列模型的发展历程进行回溯，并介绍了其在思维链框架以及模型架构中的关键创新，最后就其思维链的安全性问题展开讨论。

 The emergence of DeepSeek R1 marks a historic paradigm shift in artificial intelligence research. This groundbreaking achievement overturns the long-held dogma of traditional AI systems reliant on probabilistic generation and pattern matching. Through the deep integration of chain-of-thought and reinforcement learning, it pioneers industrial-scale AI reasoning capabilities for the first time. Its innovative reasoning architecture not only dramatically reduces the cost of training specialized AI models but also demonstrates human-like chain-of-thought characteristics in complex tasks such as mathematical proofs and logical reasoning, driving a profound transformation from probabilistic generation to causal reasoning and ushering in a new era of AI technological development. This paper traces the evolutionary trajectory of the DeepSeek model series, highlights key innovations in its chain-of thought framework and model architecture, and concludes with a discussion on the safety implications of its chain-of-thought.