Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an answer for a given input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly "thinking" about a problem before providing an answer. This reasoning process is publicly available to the user, creating endless opportunities for studying the reasoning behaviour of the model and opening up the field of Thoughtology. Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning, our analyses on DeepSeek-R1 investigate the impact and controllability of thought length, management of long or confusing contexts, cultural and safety concerns, and the status of DeepSeek-R1 vis-à-vis cognitive phenomena, such as human-like language processing and world modelling. Our findings paint a nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning, where extra inference time can impair model performance. Furthermore, we find a tendency for DeepSeek-R1 to persistently ruminate on previously explored problem formulations, obstructing further exploration. We also note strong safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning counterpart, which can also compromise safety-aligned LLMs.

翻译：以 DeepSeek-R1 为代表的大型推理模型标志着大语言模型处理复杂问题方式的根本性转变。DeepSeek-R1 并非直接为给定输入生成答案，而是构建详细的多步骤推理链，在给出答案前似乎对问题进行“思考”。该推理过程对用户公开，为研究模型的推理行为提供了无限可能，并由此开辟了“思维学”这一研究领域。基于对 DeepSeek-R1 推理基本构建模块的分类，我们的分析探讨了思维长度的影响与可控性、长上下文或混淆上下文的管理、文化及安全问题，以及 DeepSeek-R1 在类人语言处理与世界建模等认知现象方面的表现。我们的研究呈现出一幅细致入微的图景。特别地，我们发现 DeepSeek-R1 存在一个推理“最佳区间”，额外的推理时间反而可能损害模型性能。此外，我们发现 DeepSeek-R1 倾向于持续纠结于已探索过的问题表述，阻碍进一步探索。我们还注意到，相较于其非推理版本，DeepSeek-R1 存在显著的安全脆弱性，这也可能危及经过安全对齐的大语言模型。