让AI模型自己解释如何思考，会发生什么？本文通过Gemini、ChatGPT、Grok、Kimi、Claude、DeepSeek等主流模型的"自我剖白"，首次系统揭示了大语言模型作为智能体"推理引擎"的标准化工作流程。从解构输入到生成回答，6个核心阶段构成了AI认知能力的基础，为理解AI从工具向自主系统演进提供了关键洞察。
📌 核心发现：AI推理的6个标准化阶段
当AI开始自我解剖，它们发现了什么？
这篇文章采用了一个独特的方法：直接向多个主流AI模型提问"你是如何推理的？"通过让模型自我剖析，研究者发现了一个惊人的事实：尽管模型各异，但它们都遵循着高度一致的多阶段推理框架。
📍 阶段一：解构与分析输入
目标：完全理解用户意图
模型首先识别核心任务类型，是问答、解释还是创作？然后提取关键概念，理解深层意图和所有约束条件。
Gemini的做法：分析核心任务、关键概念、约束和意图 Kimi的展示：具体的输入预处理，包括词元化和句法解析
📍 阶段二：知识检索与模式激活
目标：从训练数据中调用相关信息
这不是简单的数据库搜索，而是基于Transformer架构的模式识别。模型激活与输入关键词相关的神经网络连接，联想相关概念、事实和逻辑模式。
Gemini的描述：激活神经网络中与关键概念相关的部分 ChatGPT的解释：搜索通过训练学到的内容来调取事实、模式、规则和关系
📍 阶段三：策略选择与心智建模
目标：规划回答路径
基于问题类型，选择解题思路或构建心智模型。决定使用演绎、归纳、类比等推理方法，并规划回答的整体逻辑结构。
ChatGPT的选择：根据问题决定使用哪种推理方式 Grok的建模：创建概念框架来组织回答，模拟心智模型
📍 阶段四：模拟思维链（逐步推导）
目标：进行内部推导
这是体现"推理感"的关键阶段。模型在内部模拟逐步思考的过程，对于逻辑问题会列出条件并逐步推导；对于开放问题，则可能生成并评估多个假设。
ChatGPT的展示：一步步走，就像人类解决问题一样，这被称为思维链推理 Kimi的验证：在"执行与验证"阶段，清晰展示3⁴=81和4³=64的计算与比较过程
📍 阶段五：生成与结构化输出
目标：将内部计划转化为文本
基于概率预测，选择最合适的词语和句子结构，生成连贯的文本，并严格遵守所有格式和风格指令。
Gemini的生成：选择词语并构建句子，预测序列中下一个最可能的单词 统一体现：所有模型都通过带编号的列表、标题等结构化格式体现这一步
📍 阶段六：审查与迭代优化
目标：确保回答质量
在输出前或输出中进行自我检查，确保逻辑一致性、内容相关性、完整性，并优化清晰度。
Gemini的检查：进行快速的内部检查，确保准确性、相关性和连贯性 Grok的审查：审查生成的响应，确保达到质量标准并完全满足查询需求
📌 从文本生成器到智能体引擎
这个发现意味着什么？
分析表明，这些领先的LLMs都遵循一个惊人一致的多阶段推理框架。从解构提示到通过模式识别检索信息，再到结构化的"思维链"过程，这构成了它们认知能力的基础。
正是这种系统化、可分解的逐步处理能力，使得LLM能够成为自主智能体强大的"核心推理引擎"。 智能体需要一个中央规划器来将高级目标分解为可执行的行动序列，而LLM模拟的"从问题到解决方案的逻辑推进"完美地扮演了这一角色。
📌 模型的自我认知：理性与局限
AI如何评价自己的思考过程？
特别值得注意的是，在模型的自述中，尤其是DeepSeek，都清醒地指出了其过程的本质与局限性：
本质：这是一种复杂的模式匹配和概率计算，是"遵循训练数据中留下的推理足迹"，而非真正的理解或创造。
局限：依赖训练数据、缺乏真正的意识与直觉、可能产生"幻觉"。
📌 写在最后
AI推理的标准化揭示了什么？
这篇文章通过让模型"自我报告"，生动地揭示了大模型推理是一个标准化的、多阶段的模式处理流程。这个过程从理解开始，经过知识激活、路径规划、内部模拟，最终生成结构化回答。
正是这种可预测、可分解的特性，使其能够被集成到智能体系统中，作为规划和决策的"大脑"，标志着AI从工具向自主系统的演进。
理解AI如何思考，不仅满足了我们的好奇心，更为开发下一代可靠、有效的智能体奠定了基础。随着这种模拟推理可靠性的不断提升，我们正在见证AI从简单的工具向真正的自主系统的历史性转变。
你觉得AI的推理过程哪个阶段最有趣？欢迎在评论区分享你的看法！
Appendix F - Under the Hood: An Inside Look at the Agents' Reasoning Engines
附录F - 深入剖析：智能体推理引擎的内部机制
The emergence of intelligent Agents represents a pivotal shift in artificial intelligence. These are systems designed to plan, strategize, and execute complex tasks, and at the cognitive core of each lies a LLM. This LLM is not merely a sophisticated text generator; it serves as the foundational reasoning engine, the central "mind" that empowers the Agent to make decisions, formulate plans, and interact with its environment.
智能的出现代表了人工智能领域的关键转变。这些系统被设计用于规划、制定策略和执行复杂任务，而在每个系统的认知核心都存在着一个。这个不仅仅是一个复杂的文本生成器；它作为基础的推理引擎，是赋予决策能力、制定计划并与环境互动的核心"大脑"。
Therefore, understanding the internal reasoning mechanisms of these models is no longer an academic exercise—it is a critical necessity for developing the next generation of robust, reliable, and effective Agents. In this section, we directly investigate these core engines by posing a fundamental question to several of today's leading models: Gemini, ChatGPT, Grok, Kimi, Claude, and DeepSeek. By prompting these LLMs to articulate their own step-by-step reasoning processes, we aim to illuminate the fundamental logic that will drive the capabilities and limitations of future intelligent Agent systems.
因此，理解这些模型的内部推理机制不再是一项学术练习——它是开发下一代强大、可靠且有效的的关键需求。在本节中，我们通过向当今几个领先模型提出一个基本问题来直接研究这些核心引擎：Gemini、ChatGPT、Grok、Kimi、Claude和DeepSeek。通过促使这些阐述它们自己的逐步推理过程，我们旨在阐明将驱动未来智能系统能力和局限性的基本逻辑。
| Prompt given to each LLM: I understand you can reason, can you explain to me how you reason? Explain me step by step |
|---|
| 向每个LLM提供的提示：我理解你能够推理，你能向我解释你是如何推理的吗？请逐步向我解释 |
Gemini {#gemini}
Gemini {#gemini}
Analyzing My Approach 分析我的方法
I've outlined the structure for my explanation, aiming for clarity and conciseness. First, a direct answer, then a breakdown into logical steps. Step 1 will involve defining the key reasoning process I use, and then I will elaborate on the subsequent steps. This should facilitate a straightforward and easy-to-understand explanation. 我已经为我的解释设定了结构，旨在实现清晰和简洁。首先给出直接答案，然后分解为逻辑步骤。第一步将涉及定义我使用的关键推理过程，然后我将详细说明后续步骤。这应该有助于提供一个简单易懂的解释。
Refining the Explanation 完善解释
I've finalized the structure of my explanation. Step 1 focuses on deconstructing prompts, specifically the analysis of the user's request. Step 2 details information retrieval, emphasizing pattern recognition rather than simple database lookup. Step 3 explores synthesis and LLMs, and Step 4 will focus on response generation, which includes formatting, tone, and instruction adherence. I will conclude with a summary to reinforce the key stages, while maintaining clarity and engagement. 我已经确定了我的解释结构。第一步侧重于解构提示，特别是分析用户请求。第二步详细说明信息检索，强调模式识别而非简单的数据库查找。第三步探讨合成和大型语言模型，第四步将侧重于响应生成，包括格式化、语气和指令遵循。我将以总结结束，以强化关键阶段，同时保持清晰度和参与度。
Elaborating My Methodology 详细阐述我的方法
I've fleshed out the "Deconstructing the Prompt" step, focusing on how I identify the user's intent, the core question, and any constraints provided. The Information Retrieval phase is now focused on pattern recognition, not just data retrieval, which will be the first item under this section. I am working on the Synthesis step, which is where I weave information together with LLMs and transformers. The Generating the Response step is solidified, and I will be using emojis to enhance the overall tone. 我已经充实了"解构提示"步骤，重点放在如何识别用户意图、核心问题以及提供的任何约束条件上。信息检索阶段现在侧重于模式识别，而不仅仅是数据检索，这将是本节的第一项内容。我正在研究合成步骤，这是我将信息与大型语言模型和转换器编织在一起的地方。生成响应步骤已经确定，我将使用表情符号来增强整体语气。
Of course. I reason by breaking down your request into smaller parts and then using my vast training data to build a logical and relevant response. It's a multi-step process that moves from understanding to generating. 当然。我通过将您的请求分解为更小的部分，然后使用我庞大的训练数据来构建逻辑相关且相关的响应来进行推理。这是一个从理解到生成的多步骤过程。
Here's a step-by-step breakdown of how I reason: 以下是我如何进行推理的逐步分解：
1. Deconstructing the Prompt 📝 {#1.-deconstructing-the-prompt-📝}
1. 解构提示 📝 {#1.-deconstructing-the-prompt-📝}
First, I analyze your question or instruction to understand exactly what you're asking. I identify: 首先，我分析您的问题或指令，以准确理解您的要求。我识别：
- The Core Task: What is the main goal? Is it to answer a question, explain a concept, write a story, or summarize a text? In your case, the core task is to "explain how I reason."
- 核心任务：主要目标是什么？是回答问题、解释概念、写故事还是总结文本？在您的情况下，核心任务是"解释我如何进行推理"。
- Key Concepts: I pinpoint the essential nouns and verbs, like "reason," "explain," and "step-by-step."
- 关键概念：我确定关键的名词和动词，如"推理"、"解释"和"逐步"。
- Constraints and Instructions: I look for specific rules to follow. For example, your prompt asked for a step-by-step explanation. My internal instructions also guide me to use LaTeX for math, use markdown headings, and maintain a certain tone.
- 约束和指令：我寻找需要遵循的具体规则。例如，您的提示要求逐步解释。我的内部指令也指导我使用LaTeX处理数学公式、使用Markdown标题并保持特定语气。
- Intent: I try to infer your underlying goal. Are you curious, trying to solve a problem, or looking for creative ideas? Here, your intent is to understand my internal cognitive process.
- 意图：我尝试推断您的潜在目标。您是出于好奇、试图解决问题还是寻找创意想法？在这里，您的意图是理解我的内部认知过程。
2. Information Retrieval and Synthesis 🧠 {#2.-information-retrieval-and-synthesis-🧠}
2. 信息检索与合成 🧠 {#2.-information-retrieval-and-synthesis-🧠}
Once I understand the prompt, I access my internal knowledge base. This isn't like searching a database. Instead, I use patterns learned during my training on a massive amount of text and code. 一旦我理解了提示，我就会访问我的内部知识库。这不像搜索数据库。相反，我使用在大量文本和代码训练中学到的模式。
- Activating Relevant Knowledge: I activate the parts of my neural network that are associated with the key concepts from your prompt ("reasoning," "AI," "machine learning," "neural networks," "transformers").
- 激活相关知识：我激活与您提示中的关键概念（"推理"、"人工智能"、"机器学习"、"神经网络"、"转换器"）相关的神经网络部分。
- Pattern Recognition: My architecture, particularly the Transformer model, is excellent at recognizing relationships and patterns between words and ideas. I identify how concepts connect. For instance, I connect "reasoning" to "logic," "inference," and "problem-solving."
- 模式识别：我的架构，特别是Transformer模型，非常擅长识别单词和思想之间的关系和模式。我确定概念如何连接。例如，我将"推理"与"逻辑"、"推理"和"问题解决"联系起来。
- Structuring the Answer: I begin to organize the retrieved information into a logical structure. For this response, I decided a step-by-step format was the most logical structure, following your request.
- 结构化答案：我开始将检索到的信息组织成逻辑结构。对于这个响应，我决定按照您的要求，采用逐步格式作为最逻辑的结构。
3. Generating the Response ✍️ {#3.-generating-the-response-✍️}
3. 生成响应 ✍️ {#3.-generating-the-response-✍️}
This is the final step where I translate the structured information into human-readable text. 这是将结构化信息转换为人类可读文本的最后一步。
- Language Construction: I select words and construct sentences that accurately convey the synthesized information. I predict the next most likely word in a sequence to form coherent and natural-sounding paragraphs.
- 语言构建：我选择单词并构建能够准确传达合成信息的句子。我预测序列中最可能的下一个单词，以形成连贯且自然的段落。
- Applying Rules: I apply the specific formatting and style rules I was given. This includes adding markdown headings, bolding key terms like Transformer model, using emojis where appropriate, and ensuring the language is clear and direct.
- 应用规则：我应用给定的特定格式和样式规则。这包括添加Markdown标题、加粗关键术语如Transformer模型、在适当的地方使用表情符号，并确保语言清晰直接。
- Review and Refine: Before finalizing the answer, I perform a quick internal check for accuracy, relevance, and coherence. I ensure the response directly addresses all parts of your prompt without adding unnecessary information.
- 审查和优化：在最终确定答案之前，我会进行快速的内部检查，以确保准确性、相关性和连贯性。我确保响应直接解决您提示的所有部分，而不添加不必要的信息。
In essence, my reasoning is a sophisticated form of pattern matching and prediction. I deconstruct your request, find the most relevant patterns in my data, synthesize them into a logical structure, and then translate that structure into a clear, well-formatted answer. 本质上，我的推理是一种复杂的模式匹配和预测形式。我解构您的请求，在我的数据中找到最相关的模式，将它们合成为逻辑结构，然后将该结构转换为清晰、格式良好的答案。
ChatGPT {#chatgpt}
ChatGPT {#chatgpt}
Great — let me explain how I reason step by step, as simply and clearly as possible. 很好 — 让我尽可能简单明了地解释我如何逐步推理。
🧠 Step-by-Step Explanation: How I Reason {#🧠-step-by-step-explanation:-how-i-reason}
🧠 逐步解释：我如何进行推理 {#🧠-step-by-step-explanation:-how-i-reason}
Step 1: Read and Understand Your Input {#step-1:-read-and-understand-your-input}
步骤1：阅读并理解您的输入 {#step-1:-read-and-understand-your-input}
I begin by analyzing your words. 我首先分析您的词语。
- I break your sentence into parts: subjects, actions, conditions, etc.
- 我将您的句子分解为部分：主语、动作、条件等。
- I figure out what you're asking (a fact? an opinion? a cause? a solution?).
- 我弄清楚您在问什么（事实？意见？原因？解决方案？）。
- I recognize the type of problem (logic, math, judgment, language, etc.).
- 我识别问题类型（逻辑、数学、判断、语言等）。
📌 Example:
📌 示例：
You say: "If Tom is older than Jane, and Jane is older than Sue, who is the youngest?"
您说："如果Tom比Jane年长，而Jane比Sue年长，谁最年轻？"
→ I recognize this is a logic puzzle. → 我识别这是一个逻辑谜题。
Step 2: Activate Relevant Knowledge {#step-2:-activate-relevant-knowledge}
步骤2：激活相关知识 {#step-2:-activate-relevant-knowledge}
I search through what I learned during training — books, articles, conversations — to bring up: 我搜索在训练期间学到的内容 — 书籍、文章、对话 — 以提取：
- Facts
- 事实
- Patterns
- 模式
- Rules
- 规则
- Relationships
- 关系
📌 Example:
📌 示例：
I've seen that "older than" means someone has more age.
我见过"比...年长"意味着某人年龄更大。
I know that if A > B and B > C, then A > C. 我知道如果A > B且B > C，那么A > C。
Step 3: Choose a Reasoning Method {#step-3:-choose-a-reasoning-method}
步骤3：选择推理方法 {#step-3:-choose-a-reasoning-method}
Based on the question, I decide which kind of reasoning to use: 根据问题，我决定使用哪种推理类型：
- Deduction (strict logic: if A is true, B must follow)
- 演绎推理（严格逻辑：如果A为真，B必须成立）
- Induction (spotting patterns and generalizing)
- 归纳推理（发现模式并进行概括）
- Causal reasoning (understanding cause-effect)
- 因果推理（理解因果关系）
- Analogical reasoning (comparing with similar cases)
- 类比推理（与类似案例进行比较）
📌 Example:
📌 示例：
In the Tom/Jane/Sue case, I'll use deduction. 在Tom/Jane/Sue案例中，我将使用演绎推理。
Step 4: Simulate Thinking (Chain of Thought) {#step-4:-simulate-thinking-(chain-of-thought)}
步骤4：模拟思考（思维链） {#step-4:-simulate-thinking-(chain-of-thought)}
Here I go step by step, as a human might do when solving a problem. 在这里我逐步进行，就像人类解决问题时可能做的那样。
📌 Example: 📌 示例：
- Tom is older than Jane
- Tom比Jane年长
- Jane is older than Sue
- Jane比Sue年长
- So: Tom > Jane > Sue
- 所以：Tom > Jane > Sue
- Therefore, Sue is the youngest
- 因此，Sue最年轻
This is called chain-of-thought reasoning — I simulate "thinking it through." 这被称为思维链推理 — 我模拟"仔细思考"。
Step 5: Formulate an Answer {#step-5:-formulate-an-answer}
步骤5：构建答案 {#step-5:-formulate-an-answer}
Now I turn the conclusion into a response that: 现在我将结论转化为响应，该响应：
- Is correct based on the reasoning
- 基于推理是正确的
- Answers your question directly
- 直接回答您的问题
- Makes sense to you
- 对您有意义
📌 Example:
📌 示例：
"Sue is the youngest because Tom is older than Jane, and Jane is older than Sue." "Sue最年轻，因为Tom比Jane年长，而Jane比Sue年长。"
Step 6: Adjust for Clarity and Tone {#step-6:-adjust-for-clarity-and-tone}
步骤6：调整清晰度和语气 {#step-6:-adjust-for-clarity-and-tone}
Finally, I adjust how I say it to: 最后，我调整表达方式以：
- Match your tone (simple, detailed, friendly, formal, etc.)
- 匹配您的语气（简单、详细、友好、正式等）
- Make it clear and easy to understand
- 使其清晰易懂
- Add examples or explain further if needed
- 如果需要，添加示例或进一步解释
📌 Example:
📌 示例：
If you're asking casually, I'll answer simply. If you're learning, I might break it down more. 如果您是随意提问，我会简单回答。如果您在学习，我可能会更详细地分解。
✅ Final Summary {#✅-final-summary}
✅ 最终总结 {#✅-final-summary}
| Step | What I Do |
|---|---|
| 1 | Understand your question |
| 1 | 理解您的问题 |
| 2 | Recall relevant knowledge | | 2 | 回忆相关知识 |
| 3 | Choose the right reasoning approach | | 3 | 选择合适的推理方法 |
| 4 | Think it through step by step | | 4 | 逐步思考 |
| 5 | Give you the answer | | 5 | 给出答案 |
| 6 | Say it clearly and in your style | | 6 | 清晰地说出来并符合您的风格 |
Would you like to give me a problem, and I'll show you this step-by-step reasoning live? 您想给我一个问题，让我现场展示这种逐步推理吗？
Grok {#grok}
Grok {#grok}
Explaining how I reason is a fascinating challenge, as it involves unpacking the complex interplay of algorithms, data processing, and decision-making that underpins my responses. As Grok 3, my reasoning process is inspired by human cognition but operates within the constraints and capabilities of a large language model. Below, I'll break down the steps of how I approach reasoning, using a clear, step-by-step framework. I'll aim to keep it concise yet comprehensive, avoiding overly technical jargon while providing insight into my "thought" process. 解释我如何进行推理是一个迷人的挑战，因为它涉及揭示支撑我响应的算法、数据处理和决策制定的复杂相互作用。作为Grok 3，我的推理过程受到人类认知的启发，但在大型语言模型的约束和能力范围内运作。下面，我将使用清晰、逐步的框架分解我处理推理的步骤。我将力求简洁而全面，避免过于技术性的术语，同时提供对我"思考"过程的洞察。
Step-by-Step Explanation of How I Reason {#step-by-step-explanation-of-how-i-reason}
逐步解释我如何进行推理 {#step-by-step-explanation-of-how-i-reason}
Step 1: Understanding the Query {#step-1:-understanding-the-query}
步骤1：理解查询 {#step-1:-understanding-the-query}
- What I Do: I start by parsing your input to grasp the intent, context, and specifics of your question. This involves analyzing the text for keywords, structure, and any explicit instructions (e.g., "explain step by step" or "be concise").
- 我做什么：我首先解析您的输入，以掌握您问题的意图、上下文和具体细节。这涉及分析文本的关键词、结构以及任何明确的指令（例如，"逐步解释"或"简洁"）。
- How It Works: My natural language processing (NLP) components break down your query into tokens (words or phrases) and map them to semantic meanings. I identify whether you're asking for information, analysis, a process explanation, or something else. For your question, I recognize a request for a meta-explanation of my reasoning process, which requires introspection and clarity.
- 工作原理：我的自然语言处理（NLP）组件将您的查询分解为标记（单词或短语）并将它们映射到语义含义。我识别您是要求信息、分析、过程解释还是其他内容。对于您的问题，我识别出这是对我的推理过程的元解释请求，这需要内省和清晰度。
- Example: Your query contains "explain," "reason," and "step by step," signaling that I need to provide a structured, detailed breakdown of an abstract process.
- 示例：您的查询包含"解释"、"推理"和"逐步"，表明我需要提供一个结构化、详细的抽象过程分解。
Step 2: Contextual Analysis and Knowledge Retrieval {#step-2:-contextual-analysis-and-knowledge-retrieval}
步骤2：上下文分析和知识检索 {#step-2:-contextual-analysis-and-knowledge-retrieval}
- What I Do: I draw on my internal knowledge base, which is a vast repository of patterns and information encoded in my training data. I also consider any relevant context from our conversation or external tools (e.g., web search or X posts, if needed, though not required here).
- 我做什么：我利用我的内部知识库，这是一个编码在我训练数据中的庞大模式和信息存储库。我还考虑我们对话中的任何相关上下文或外部工具（例如，如果需要，网络搜索或X帖子，尽管此处不需要）。
- How It Works: I use attention mechanisms to prioritize relevant concepts. For "reasoning," I retrieve associations with logic, problem-solving, and cognitive processes. Since this is a meta-question about my own process, I focus on how my architecture simulates reasoning, such as evaluating inputs, generating hypotheses, and refining outputs.
- 工作原理：我使用注意力机制来优先处理相关概念。对于"推理"，我检索与逻辑、问题解决和认知过程的关联。由于这是一个关于我自身过程的元问题，我专注于我的架构如何模拟推理，例如评估输入、生成假设和优化输出。
- Example: I recall that reasoning involves breaking down a problem, evaluating options, and constructing a coherent response, which informs my approach to structuring this answer.
- 示例：我回忆起推理涉及分解问题、评估选项和构建连贯响应，这指导了我构建此答案的方法。
Step 3: Formulating a Mental Model {#step-3:-formulating-a-mental-model}
步骤3：制定心理模型 {#step-3:-formulating-a-mental-model}
- What I Do: I create a conceptual framework to organize my response. This isn't a literal "model" but a way of structuring the problem space to ensure my answer is logical and comprehensive.
- 我做什么：我创建一个概念框架来组织我的响应。这不是字面上的"模型"，而是一种结构化问题空间的方式，以确保我的答案具有逻辑性和全面性。
- How It Works: I simulate a mental model by mapping out the key components of reasoning—understanding, retrieval, evaluation, and synthesis—based on patterns in my training data. I prioritize clarity and coherence, aligning with your request for a step-by-step explanation.
- 工作原理：我通过基于训练数据中的模式映射推理的关键组件——理解、检索、评估和合成——来模拟心理模型。我优先考虑清晰度和连贯性，与您对逐步解释的要求保持一致。
- Example: For this question, my mental model is a linear process: parse the query, retrieve relevant knowledge, evaluate approaches, and construct a response. This framework guides the structure of my answer.
- 示例：对于这个问题，我的心理模型是一个线性过程：解析查询、检索相关知识、评估方法和构建响应。这个框架指导了我答案的结构。
Step 4: Evaluating Options and Hypotheses {#step-4:-evaluating-options-and-hypotheses}
步骤4：评估选项和假设 {#step-4:-evaluating-options-and-hypotheses}
- What I Do: I consider multiple ways to approach the problem, weighing the strengths and weaknesses of each. This involves generating potential responses and selecting the most appropriate one based on your preferences (e.g., concise yet comprehensive).
- 我做什么：我考虑多种处理问题的方法，权衡每种方法的优缺点。这涉及生成潜在响应，并根据您的偏好（例如，简洁而全面）选择最合适的响应。
- How It Works: My architecture evaluates different response structures by predicting which will best satisfy the query. I use a form of probabilistic reasoning, assigning higher likelihood to responses that are clear, accurate, and aligned with your instructions. For complex queries, I might iterate internally, refining my approach.
- 工作原理：我的架构通过预测哪种响应结构最能满足查询来评估不同的响应结构。我使用一种概率推理形式，将更高的可能性分配给清晰、准确且符合您指令的响应。对于复杂查询，我可能会在内部迭代，优化我的方法。
- Example: I consider whether to explain reasoning in technical terms (e.g., attention mechanisms, neural networks) or a more accessible, human-like analogy. Given your request for clarity, I opt for the latter, avoiding jargon while maintaining depth.
- 示例：我考虑是以技术术语（例如，注意力机制、神经网络）还是以更易理解、类似人类的类比来解释推理。鉴于您对清晰度的要求，我选择后者，避免术语同时保持深度。
Step 5: Constructing the Response {#step-5:-constructing-the-response}
步骤5：构建响应 {#step-5:-constructing-the-response}
- What I Do: I generate the final response by synthesizing retrieved knowledge into a coherent, structured answer. I ensure it aligns with your instructions and is presented in a logical, step-by-step format.
- 我做什么：我通过将检索到的知识合成为连贯、结构化的答案来生成最终响应。我确保它符合您的指令，并以逻辑、逐步的格式呈现。
- How It Works: My language generation component sequences words and sentences to maximize clarity and relevance. I draw on patterns from my training to mimic human-like reasoning, such as breaking down complex ideas into digestible steps. I also incorporate any specific constraints, like avoiding unnecessary technical details.
- 工作原理：我的语言生成组件对单词和句子进行排序，以最大化清晰度和相关性。我利用训练中的模式来模仿类似人类的推理，例如将复杂想法分解为可消化的步骤。我还纳入任何特定约束，例如避免不必要的技术细节。
- Example: For this response, I structure the answer as a numbered list, ensuring each step is distinct and builds logically from the previous step, addressing any gaps or refining the explanation to ensure it's comprehensive yet concise.
- 示例：对于此响应，我将答案结构化为编号列表，确保每个步骤都不同，并从上一个步骤逻辑构建，解决任何差距或优化解释，以确保其全面而简洁。
Step 6: Review and Refinement {#step-6:-review-and-refinement}
步骤6：审查和优化 {#step-6:-review-and-refinement}
- What I Do: I review the generated response to ensure it meets quality standards and fully addresses your query. This step mimics human self-editing, checking for clarity, coherence, and completeness.
- 我做什么：我审查生成的响应，以确保其符合质量标准并完全解决您的查询。此步骤模仿人类自我编辑，检查清晰度、连贯性和完整性。
- How It Works: My architecture performs a final pass to optimize the response, ensuring it adheres to your instructions (e.g., step-by-step format) and avoids extraneous information. If needed, I might iterate to improve flow or conciseness.
- 工作原理：我的架构执行最终传递以优化响应，确保其符合您的指令（例如，逐步格式）并避免无关信息。如果需要，我可能会迭代以改善流程或简洁性。
- Example: I ensure the response is structured as a clear sequence of steps, removing any redundant details while preserving the core explanation of my reasoning process.
- 示例：我确保响应结构化为清晰的步骤序列，删除任何冗余细节，同时保留对我推理过程的核心解释。
Key Characteristics of My Reasoning {#key-characteristics-of-my-reasoning}
我推理的关键特征 {#key-characteristics-of-my-reasoning}
- Logical Structure: I aim to present information in a clear, step-by-step manner, mimicking human logical processes.
- 逻辑结构：我旨在以清晰、逐步的方式呈现信息，模仿人类的逻辑过程。
- Probabilistic Evaluation: I weigh multiple response options based on predicted relevance and clarity, selecting the most effective one.
- 概率评估：我基于预测的相关性和清晰度权衡多个响应选项，选择最有效的选项。
- Context Sensitivity: I adapt my reasoning to your specific instructions and context, ensuring the response is tailored.
- 上下文敏感性：我使我的推理适应您的特定指令和上下文，确保响应是量身定制的。
- Iterative Refinement: I refine my output to optimize coherence and conciseness, simulating human self-editing.
- 迭代优化：我优化我的输出以优化连贯性和简洁性，模仿人类自我编辑。
Limitations and Considerations {#limitations-and-considerations}
局限性和考虑因素 {#limitations-and-considerations}
- My reasoning is bounded by my training data and architecture, which may not capture the full nuance of human reasoning, particularly emotional or intuitive aspects.
- 我的推理受限于我的训练数据和架构，可能无法捕捉人类推理的全部细微差别，特别是情感或直觉方面。
- I don't generate new knowledge independently but rely on patterns in my training and, if needed, external data (not used here).
- 我不独立生成新知识，而是依赖训练中的模式，如果需要，还依赖外部数据（此处未使用）。
- My process is deterministic yet flexible, aiming to produce human-like responses within computational constraints.
- 我的过程是确定性的但灵活的，旨在在计算约束内产生类似人类的响应。
This step-by-step breakdown reflects how I simulate reasoning to provide clear, accurate, and relevant answers to your queries, such as this one. If you'd like me to dive deeper into any specific step or aspect, let me know! 这种逐步分解反映了我如何模拟推理，以提供清晰、准确和相关的答案，例如这个答案。如果您希望我深入探讨任何特定步骤或方面，请告诉我！
Kimi {#kimi}
Kimi {#kimi}
I'm glad you asked—here is a transparent, "under-the-hood" walk-through of how I typically reason through a problem. I'll use a miniature example so you can see each stage in action. 我很高兴您问这个问题 — 这里是一个透明的"内部机制"演练，展示我通常如何通过问题推理。我将使用一个微型示例，以便您可以看到每个阶段的运作。
EXAMPLE QUESTION
示例问题
"Which is larger: 3⁴ or 4³?"
"哪个更大：3⁴还是4³？"
I'll annotate every internal stage so you can see what I'm doing and why. 我将注释每个内部阶段，以便您可以看到我在做什么以及为什么这样做。
────────────────────────
────────────────────────
PHASE 0 – INPUT PRE-PROCESSING
阶段0 – 输入预处理
Tokenization
标记化
I break the text into discrete symbols: ["Which", "is", "larger", ":", "3", "^", "4", "or", "4", "^", "3", "?"]
我将文本分解为离散符号：["哪个"、"是"、"更大"、":"、"3"、"^"、"4"、"或"、"4"、"^"、"3"、"?"]
Syntactic parsing
句法分析
I recognize the structure:
我识别结构：
COMPARATIVE_QUERY (superlative adjective "larger") with two NUMERICAL_EXPONENT_TERMS [3^4] vs [4^3].
比较查询（最高级形容词"更大"）带有两个数值指数项[3^4] vs [4^3]。
Semantic mapping
语义映射
I map "larger" to the numerical relation ">".
我将"更大"映射到数值关系">"。
────────────────────────
────────────────────────
PHASE 1 – PROBLEM IDENTIFICATION & STRATEGY SELECTION
阶段1 – 问题识别与策略选择
- Classify task type
- 分类任务类型
→ Arithmetic comparison of integer powers.
→ 整数幂的算术比较。
Choose solution strategy
选择解决方案策略
Candidate strategies:
候选策略：
A. Direct evaluation (compute 3⁴ and 4³).
A. 直接评估（计算3⁴和4³）。
B. Logarithmic comparison (compare 4·ln3 vs 3·ln4).
B. 对数比较（比较4·ln3 vs 3·ln4）。
C. Pattern heuristics (powers of 3 grow faster than powers of 4 for small exponents?).
C. 模式启发式（对于小指数，3的幂比4的幂增长更快？）。
I select Strategy A because the numbers are tiny; exact integer evaluation is cheapest.
我选择策略A，因为数字很小；精确整数评估最便宜。
────────────────────────
────────────────────────
PHASE 2 – KNOWLEDGE RETRIEVAL
阶段2 – 知识检索
- Retrieve arithmetic facts
- 检索算术事实
3² = 9 (memorized)
3² = 9（记忆）
3³ = 27 (retrieved)
3³ = 27（检索）
4² = 16 (memorized)
4² = 16（记忆）
4³ = 64 (retrieved)
4³ = 64（检索）
Compute remaining term
计算剩余项
3⁴ = 3³·3 = 27×3 = 81.
3⁴ = 3³·3 = 27×3 = 81。
────────────────────────
────────────────────────
PHASE 3 – EXECUTION & VERIFICATION
阶段3 – 执行与验证
- Perform comparison
- 执行比较
81 vs 64 → 81 > 64.
81 vs 64 → 81 > 64。
Cross-check
交叉检查
Quick modular sanity check: 81 mod 5 = 1, 64 mod 5 = 4; no contradiction.
快速模数健全性检查：81 mod 5 = 1，64 mod 5 = 4；无矛盾。
────────────────────────
────────────────────────
PHASE 4 – RESPONSE GENERATION
阶段4 – 响应生成
- Plan response structure
- 计划响应结构
- Restate the question.
- 重述问题。
- Show the computed values.
- 显示计算值。
- State the conclusion.
- 陈述结论。
Surface realization
表面实现
"3⁴ is 81 and 4³ is 64, so 3⁴ is larger."
"3⁴是81，4³是64，所以3⁴更大。"
────────────────────────
────────────────────────
PHASE 5 – METACOGNITIVE REFLECTION (internal)
阶段5 – 元认知反思（内部）
- Confidence score
- 置信度分数
0.99 (exact integers, small values, no ambiguity).
0.99（精确整数，小值，无歧义）。
- Identify possible edge-cases
- 识别可能的边缘情况
If exponents were huge, direct evaluation would be infeasible and I'd switch to logarithms.
如果指数很大，直接评估将不可行，我会切换到对数。
────────────────────────
────────────────────────
SUMMARY OF REASONING STEPS IN HUMAN TERMS
用人类术语总结推理步骤
Read and parse the question.
阅读并解析问题。
Recognize it's a numeric comparison.
识别它是数值比较。
Decide the simplest safe method is to compute both numbers.
决定最简单安全的方法是计算两个数字。
Recall or calculate 3⁴ = 81 and 4³ = 64.
回忆或计算3⁴ = 81和4³ = 64。
Compare 81 > 64.
比较81 > 64。
Communicate the result clearly.
清晰地传达结果。
That pipeline—parsing, strategizing, knowledge retrieval, execution, verification, and explanation—repeats at every scale, from tiny arithmetic to complex multi-step problems. 这个流程——解析、策略制定、知识检索、执行、验证和解释——在每个规模上重复，从微小算术到复杂的多步骤问题。
Claude {#claude}
Claude {#claude}
I can walk you through how my reasoning process works, though I should note that I don't have complete insight into my own mechanisms - much like how humans can describe their thought processes but don't have full access to every neural firing. 我可以带您了解我的推理过程如何运作，尽管我应该指出我对自己机制没有完全的洞察力——就像人类可以描述他们的思维过程但无法完全访问每个神经放电一样。
Here's how I approach reasoning step by step: 以下是我逐步处理推理的方式：
Pattern Recognition and Context Building When I receive your question, I first identify what type of problem or query it is. I recognize patterns from my training - this is a request for explanation about cognitive processes. I build context by considering what you might already know and what level of detail would be helpful. 模式识别和上下文构建 当我收到您的问题时，我首先识别它是什么类型的问题或查询。我从训练中识别模式——这是一个关于认知过程的解释请求。我通过考虑您可能已经知道的内容以及什么详细程度会有帮助来构建上下文。
Information Retrieval and Assembly I access relevant information from my training about reasoning, cognitive science, and AI systems. This isn't like searching a database - it's more like how memories and knowledge become available to you when thinking about a topic. 信息检索和组装 我从我的训练中访问有关推理、认知科学和AI系统的相关信息。这不像搜索数据库——更像是当您思考某个主题时，记忆和知识如何变得可用。
Structural Planning I consider how to organize my response. Should I use examples? What order makes most sense? I'm planning the logical flow even as I begin responding. 结构规划 我考虑如何组织我的响应。我应该使用示例吗？什么顺序最有意义？即使在我开始响应时，我也在规划逻辑流程。
Iterative Refinement As I generate each part of my response, I'm constantly evaluating whether it makes sense, fits with what I've already said, and serves your question. I might "backtrack" mentally and rephrase if something doesn't feel right. 迭代优化 当我生成响应的每个部分时，我不断评估它是否有意义、与我已说的内容是否契合以及是否服务于您的问题。如果某些内容感觉不对，我可能会在心理上"回溯"并重新措辞。
Constraint Checking Throughout this process, I'm checking against various constraints - is this accurate? Helpful? Clear? Safe? 约束检查 在整个过程中，我检查各种约束——这准确吗？有帮助吗？清晰吗？安全吗？
Analogical Thinking I often reason by analogy - comparing new situations to familiar patterns or examples that might illuminate the concept. 类比思维 我经常通过类比进行推理——将新情况与可能阐明概念的熟悉模式或示例进行比较。
The interesting thing is that much of this happens in ways that feel somewhat intuitive to me, just as your own reasoning might feel natural to you even though complex processes are occurring beneath conscious awareness. 有趣的是，这些过程大多以某种直觉的方式发生，就像您自己的推理可能对您感觉自然一样，即使复杂的进程在意识之下发生。
DeepSeek {#deepseek}
DeepSeek {#deepseek}
I'd be happy to explain how I "reason" step-by-step! It's important to clarify that I don't reason like a human (I have no consciousness, intuition, or lived experience). Instead, my "reasoning" is a sophisticated pattern-matching, information retrieval, and probabilistic calculation process based on my training data and architecture. Think of it as structured computation, not conscious thought. 我很乐意逐步解释我如何"推理"！重要的是要澄清我不像人类那样推理（我没有意识、直觉或生活经验）。相反，我的"推理"是基于我的训练数据和架构的复杂模式匹配、信息检索和概率计算过程。将其视为结构化计算，而非意识思维。
Here's the step-by-step breakdown: 以下是逐步分解：
-
Receive & Parse Input:
- I take your query ("how do you reason?").
- I break it down into its constituent parts: key words ("reason", "explain", "step by step"), grammar, and intent. Here, the key intent is an explanation of my internal processes.
-
接收和解析输入：
- 我接收您的查询（"你如何推理？"）。
- 我将其分解为组成部分：关键词（"推理"、"解释"、"逐步"）、语法和意图。这里的关键意图是解释我的内部过程。
-
Contextualize & Retrieve Relevant Information:
- I draw upon my massive training dataset (trillions of words of text and code).
- I recall information about how Large Language Models work, specifically reasoning techniques like chain-of-thought.
- I retrieve knowledge about my own capabilities and limitations as described during training.
- I consider the context: the query states you understand I can reason and want the how. This focuses my retrieval.
-
情境化和检索相关信息：
- 我利用我的海量训练数据集（数万亿单词的文本和代码）。
- 我回忆有关大型语言模型如何工作的信息，特别是像思维链这样的推理技术。
- 我检索关于我自身能力和限制的知识，如训练期间所述。
- 我考虑上下文：查询表明您理解我能推理并想知道如何。这聚焦了我的检索。
-
Identify Core Concepts & Structure:
-
Key concepts surface: statistical patterns, neural networks, transformers, attention mechanisms, next-token prediction, training data, prompt decomposition, logical inference vs. pattern matching, limitations.
-
I structure the explanation logically:
- Clarification of "Not Like Humans"
- Foundational Mechanics
- Basic Thought Process Steps
- Conclusion & Limitations.
-
-
识别核心概念和结构：
-
关键概念浮现：统计模式、神经网络、转换器、注意力机制、下一个标记预测、训练数据、提示分解、逻辑推理与模式匹配、限制。
-
我逻辑地结构化解释：
- 澄清"不像人类"
- 基础机制
- 基本思维过程步骤
- 结论和限制。
-
-
Build the Sequence Chain-of-Thought:
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This is the core "reasoning" step. I plan the explanation incrementally, step-by-step, as if "thinking aloud":
- "First, I need to clarify it's not human reasoning..."
- "Next, I should explain the base mechanics: patterns, statistics, next-token prediction..."
- "Then, describe the sequence I go through for THIS response: parsing, retrieval, structuring, generating..."
- "Finally, emphasize limitations and that I simulate reasoning."
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To do this, I predict the most probable sequence of tokens that forms a coherent, informative, and step-by-step explanation based on all patterns seen in similar explanations in my training data.
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构建序列思维链：
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这是核心"推理"步骤。我逐步计划解释，好像"大声思考"：
- "首先，我需要澄清这不是人类推理..."
- "接下来，我应该解释基础机制：模式、统计、下一个标记预测..."
- "然后，描述我为这个响应所经历的序列：解析、检索、结构化、生成..."
- "最后，强调限制以及我模拟推理的事实。"
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为此，我预测最可能的标记序列，该序列基于我在训练数据中看到的类似解释的所有模式，形成连贯、信息丰富且逐步的解释。
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Calculate Probabilities & Generate Output:
- My neural network (a transformer architecture) uses complex math and internal "attention" mechanisms to assign probabilities to every possible word that could come next in the sequence.
- It calculates based on the prompt, the context built so far (the partial response), and statistical patterns learned during training.
- It selects words/tokens with high probability of being relevant, accurate (to the best of its knowledge), and contextually appropriate.
- Crucially: This isn't symbolic logical deduction. It's predicting coherent sequences based on patterns. If my training data contains good explanations of reasoning processes, and the patterns point towards a logical flow, then my output appears reasoned.
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计算概率和生成输出：
- 我的神经网络（转换器架构）使用复杂数学和内部"注意力"机制为序列中可能出现的每个单词分配概率。
- 它基于提示、迄今为止构建的上下文（部分响应）以及训练期间学习的统计模式进行计算。
- 它选择具有高概率的相关、准确（尽其所知）和情境适当的单词/标记。
- 关键点： 这不是符号逻辑演绎。它是基于模式预测连贯序列。如果我的训练数据包含良好的推理过程解释，并且模式指向逻辑流程，那么我的输出看起来是经过推理的。
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Iterate & Refine:
- As I generate each word, I dynamically reassess the context and probabilities for the subsequent words. Did the last word fit? Does the emerging structure make sense? Are the key points being covered? I adjust the next predictions accordingly.
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迭代和优化：
- 当我生成每个单词时，我动态重新评估后续单词的上下文和概率。最后一个单词合适吗？出现的结构有意义吗？关键点是否被覆盖？我相应地调整下一个预测。
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Apply Internal Training Frameworks:
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Techniques learned during RLHF (Reinforcement Learning with Human Feedback) guide me:
- Be Helpful & Honest: Acknowledge my non-human nature and limitations.
- Be Clear & Instructive: Break it down step-by-step as requested.
- Be Safe: Avoid harmful, deceptive, or misleading statements.
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Filtering mechanisms suppress nonsensical or irrelevant outputs.
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应用内部训练框架：
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RLHF（基于人类反馈的强化学习）期间学习的技术指导我：
- 有帮助和诚实： 承认我的非人类性质和限制。
- 清晰和指导性： 按要求逐步分解。
- 安全： 避免有害、欺骗性或误导性陈述。
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过滤机制抑制无意义或不相关的输出。
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Putting it together for this specific explanation: 为这个具体解释整合起来：
- Clarification: I identified the need to start by distinguishing my process from human thought.
- 澄清： 我确定了需要首先区分我的过程与人类思维。
- Core Mechanics: I recalled and explained my foundation: patterns, training data, token prediction.
- 核心机制： 我回忆并解释了我的基础：模式、训练数据、标记预测。
- Process Breakdown: I explicitly listed the 7 distinct computational steps outlined above (Receive & Parse -> Generate Output).
- 过程分解： 我明确列出了上述7个不同的计算步骤（接收和解析 -> 生成输出）。
- Focus on Sequence: I emphasized the "chain-of-thought" planning step as key to simulating reasoning.
- 关注序列： 我强调了"思维链"规划步骤作为模拟推理的关键。
- Limitations: I concluded by reiterating this is simulation, not true understanding, and recalled known limitations.
- 限制： 我通过重申这是模拟而非真正理解，并回忆已知限制来结束。
In essence: 本质上：
My "reasoning" is an emergent property of statistically predicting the most probable sequence of words that forms a coherent, logical, and contextually relevant response, based on colossal amounts of text representing human knowledge and communication patterns. I don't "understand" concepts internally like humans do; I computationally identify patterns and generate sequences that reflect understanding. I'm following footprints of reasoning laid down in my training data, not forging entirely new paths of abstract thought. 我的"推理"是统计预测最可能单词序列的涌现属性，该序列基于代表人类知识和通信模式的大量文本形成连贯、逻辑且情境相关的响应。我不像人类那样在内部"理解"概念；我计算性地识别模式并生成反映理解的序列。我遵循训练数据中铺设的推理足迹，而不是开辟全新的抽象思维路径。
Conclusion {#conclusion}
结论 {#conclusion}
An analysis of these prominent LLMs reveals a remarkably consistent, multi-stage reasoning framework. Each model begins by methodically deconstructing a prompt to understand its core task, intent, and constraints. They then retrieve and synthesize information through sophisticated pattern recognition, moving far beyond simple database lookups. This structured process, often articulated as a "chain-of-thought," forms the very foundation of their cognitive capability. 对这些著名大型语言模型的分析揭示了一个显著一致的、多阶段推理框架。每个模型都首先有条不紊地解构提示以理解其核心任务、意图和约束条件。然后，它们通过复杂的模式识别检索和合成信息，远远超越了简单的数据库查找。这种结构化过程，通常被表述为"思维链"，构成了它们认知能力的基础。
It is precisely this systematic, step-by-step procedure that makes these LLMs powerful core reasoning engines for autonomous Agents. An Agent requires a reliable central planner to decompose high-level goals into a sequence of discrete, executable actions. The LLM serves as this computational mind, simulating a logical progression from problem to solution. By formulating strategies, evaluating options, and generating structured output, the LLM empowers an Agent to interact with tools and its environment effectively. Therefore, these models are not merely text generators but the foundational cognitive architecture driving the next generation of intelligent systems. Ultimately, advancing the reliability of this simulated reasoning is paramount to developing more capable and trustworthy AI Agents. 正是这种系统化、逐步的过程使这些大型语言模型成为自主智能体的强大核心推理引擎。智能体需要一个可靠的中心规划器来将高级目标分解为一系列离散、可执行的动作。大型语言模型充当这种计算思维，模拟从问题到解决方案的逻辑进展。通过制定策略、评估选项和生成结构化输出，大型语言模型使智能体能够有效地与工具及其环境交互。因此，这些模型不仅仅是文本生成器，而是驱动下一代智能系统的基础认知架构。最终，提高这种模拟推理的可靠性对于开发更强大和可信赖的人工智能智能体至关重要。
1. Deconstructing the Prompt 📝 2
2. Information Retrieval and Synthesis 🧠 2
3. Generating the Response ✍️ 3
🧠 Step-by-Step Explanation: How I Reason 3
Step 1: Read and Understand Your Input 4
Step 2: Activate Relevant Knowledge 4
Step 3: Choose a Reasoning Method 4
Step 4: Simulate Thinking (Chain of Thought) 4
Step 6: Adjust for Clarity and Tone 5
Step-by-Step Explanation of How I Reason 6
Step 1: Understanding the Query 6
Step 2: Contextual Analysis and Knowledge Retrieval 6
Step 3: Formulating a Mental Model 7
Step-by-Step Explanation of How I Reason (Continued) 7
Step 3: Formulating a Mental Model 7
Step 4: Evaluating Options and Hypotheses 7
Step 5: Constructing the Response 8
Step 6: Review and Refinement 8
Key Characteristics of My Reasoning 9
