{
  "schema_version": 1,
  "credential_free": true,
  "attempts": [
    {
      "attempt": 1,
      "request": {
        "model": "doubao-seed-1-6-250615",
        "messages": [
          {
            "role": "user",
            "content": "You are an exacting bilingual technical-book translation evaluator. Compare two anonymous Chinese translations against the complete English Markdown source. Score both X and Y from 1 to 5 on exactly: accuracy (no omissions, inventions, or changed claims); fluency; terminology (consistent and technically correct); markdown_code_fidelity (figures, links, headings, equations, and fenced code preserved). Each score needs concrete quoted or located evidence. Prefer one only when evidence supports it. Return JSON only: {\"variants\":{\"X\":{\"accuracy\":{\"score\":1,\"evidence\":\"...\"},\"fluency\":{\"score\":1,\"evidence\":\"...\"},\"terminology\":{\"score\":1,\"evidence\":\"...\"},\"markdown_code_fidelity\":{\"score\":1,\"evidence\":\"...\"}},\"Y\":{\"accuracy\":{\"score\":1,\"evidence\":\"...\"},\"fluency\":{\"score\":1,\"evidence\":\"...\"},\"terminology\":{\"score\":1,\"evidence\":\"...\"},\"markdown_code_fidelity\":{\"score\":1,\"evidence\":\"...\"}}},\"preferred\":\"X|Y|tie\",\"preference_evidence\":\"...\"}.\n\nCOMPLETE ENGLISH SOURCE:\nThree observations matter here. First, RL training lets the model learn when and how to use tools, so the client no longer has to hand-write the orchestration logic for tool calls. Second, the model decides when to search and what to search for, showing genuine autonomy. Third, it adjusts strategy as search results arrive and judges whether it has enough information. A common misconception is worth clarifying: **reinforcement learning gives the model the decision policy**, not the tools themselves. It teaches when to call a tool, which tool to choose, what arguments to pass, whether to continue after receiving a result, and how to chain dozens or hundreds of calls into coherent reasoning; these *whether-and-how-to-use* judgments are what get written into the model's weights. **The tools and their execution are provided by the Agent framework or API built-ins**: the implementations of `web_search` and `code_runner`, the code sandbox, and the infrastructure that issues calls and returns results all live outside the model. RL optimizes the decision policy; it does not embed a search engine or a code sandbox into the model's weights. Thus, the orchestration loop has not disappeared; it has moved from the client to the server, while decision-making has moved into the model[^ch1-2].\n\n[^ch1-2]: Thanks to reader asdlem for pointing out and clarifying, via GitHub Issue #30, the distinction that what RL internalizes is the tool-calling decision policy, not the tool execution mechanism. See https://github.com/bojieli/ai-agent-book/issues/30\n\nKimi K3’s notable advantage in Agent tasks is **the stability of long-chain tool calls**—it can sustain 200–300 consecutive tool calls with coherent reasoning throughout, far beyond the few dozen calls at which most models begin to degrade. K3 is optimized for long-horizon programming and Agent workloads, and was released in two variants: K3 Max (for dialogue and Agent tasks) and K3 Swarm Max (for large-scale parallel processing). As an open-source model, it matches top-tier closed-source systems on software engineering and Agent benchmarks—evidence that reinforcement learning can endow a model with native Agent capability.\n\n#### Experiment 1-3 ★: GPT-5.6 Native Deep Research Capability\n\nThe second experiment uses **OpenAI GPT-5.6** to show how an advanced model, backed by API-level built-in tools, closes the \"search—read—analyze\" orchestration loop on the server side for Deep Research. GPT-5.6 comes in three variants—Sol (flagship frontier model), Terra (balanced model for everyday work), and Luna (fast, economical lightweight model)—all leaving the tool-calling decisions to the model natively, so the client needs no orchestration framework of its own. One convenient feature is **Freeform Tool Calling**. Traditionally, a model calling a tool must serialize every parameter into strict JSON (a structured data format), much like filling out a form with rigid formatting rules. Freeform tool calling (declared in the API through a tool of `type: \"custom\"`) lets the model send raw text straight to the tool (a snippet of Python code, a SQL query), avoiding JSON escaping entirely. It is worth stressing that this is an evolution of the API's parameter format, not an innovation in model architecture—the client's tool-calling loop (detect `tool_calls` → execute → return the result) stays the same; only the arguments change from a JSON string to raw text. GPT-5.6 also introduces a Verbosity parameter (controlling output detail) and a Reasoning Effort parameter (adjusting reasoning depth; Sol adds a max level for the most thorough reasoning time), letting developers tune model behavior to the complexity of the task.\n\nGPT-5.6, paired with the Responses API's **web search and code interpreter** built-in tools, delivers the core mechanism of Deep Research: the model can autonomously search the web for real-time information and write code for in-depth analysis, enabling an iterative research process of \"search -> read -> analyze -> search again.\" For example, when faced with a question like \"What is the shortest distance between the capitals of the 10 ASEAN countries?\", GPT-5.6 automatically searches for the geographic coordinates of each capital, then writes Python code to calculate the great-circle distance between all pairs of capitals, ultimately identifying the closest pair. Similarly, in a task like \"Search for Bitcoin's trend over the past month and perform technical analysis,\" it can fetch real-time price data from multiple financial data sources, use professional technical analysis libraries to calculate moving averages, RSI, MACD, and other technical indicators, generate visual charts, and provide trading recommendations.\n\nMore importantly, GPT-5.6 internalizes the design philosophy of the **OpenAI Deep Research** product at the model level, introducing an **intent clarification process**. Given a research request, GPT-5.6 does not start executing immediately; it first clarifies the user's true intent through a series of questions. For \"Search for Bitcoin's trend over the past month and perform technical analysis,\" it would first ask: \"Which data source do you prefer? Which technical indicators would you like analyzed?\" This interactive clarification lets GPT-5.6 produce research reports that are more precise and better aligned with what the user actually needs.\n\nGPT-5.6 is a mature example of \"Model as Agent\"—web search, the code interpreter, and other built-in tools of the Responses API execute in a closed loop on the server; the orchestration loop moves from the client to the API server, which simplifies the client implementation. The model still emits standard tool calls; the client simply no longer has to build the \"search—read—analyze\" orchestration framework itself. Its most noteworthy aspect is the intent clarification mechanism: rather than executing a task immediately, the model first confirms what the user really needs, then formulates a research strategy. The gap between \"what the user said\" and \"what the user actually wants\" is addressed before execution begins.\n\nFigure 1-4 illustrates the complete architecture of native tool calling under the \"Model as Agent\" paradigm, along with the ReAct execution process of Kimi K3 and GPT-5.6 in real-world tasks.\n\n![Figure 1-4: \"Model as Agent\" Architecture—Native Tool Calling](images/fig1-4.svg)\n\n## Harness Engineering: Competitiveness Beyond the Model\n\nBy now you understand how an Agent works at its core: an LLM runs the ReAct loop, guided by context, using tools to complete the task. The experiments above show that the basic mechanism works—and also expose how fragile it is. The model may hallucinate (invent tools or parameters that do not exist), pick the wrong tool, or fail to recover from an error. Between a working demo and a reliable product lies a substantial gap, and those fragilities are exactly what Harness Engineering exists to fix. The first half of this chapter answered what an Agent is; the second half answers how an Agent runs reliably in production.\n\nThe preceding sections established the core formula: **Agent = LLM + Context + Tools**. It describes the Agent's **internal composition**: reasoning engine, working context, and action interfaces. Harness Engineering adds a second, **implementation-level** view of the same system: treat the LLM as one core component (the Model), and call all the supporting code built around it the Harness. The two views are not rivals; they describe the same system at different levels of abstraction. We switch to the more general word \"Model\" because the principles of Harness Engineering apply to any model that can reason and call tools, not one particular kind. The core of the Harness is the original formula's \"Context + Tools,\" plus three layers of safeguards: **Constrain** (what the Agent may and may not do), **Verify** (whether it did the thing correctly), and **Correct** (how to recover when it did not).\n\nExpanded as an equation, the complete production-grade composition is:\n\n> **Agent = LLM + [Context + Tools + Constrain + Verify + Correct] = Model + Harness**\n\nA minimal working Agent runs on LLM, context, and tools alone. To keep running reliably in long-running production workloads, it needs the three outer engineering layers as well—constrain to prevent overreach, verify to catch errors, correct to recover from failures. These layers are not standalone modules added after the fact; they are safeguards wrapped around \"Context + Tools.\" Put differently: the minimal formula is the demo view, and the expanded formula is the production view—the latter contains the former entirely and adds a safety net around it.\n\nAn example clarifies the boundaries: embedding the refund policy in the context falls under **Context**, while checking that the refund amount does not exceed the order total falls under **Constrain**. Executing an API call falls under **Tools**, while automatically retrying after the API times out falls under **Correct**. The model supplies the underlying understanding and reasoning; the Harness guides, constrains, and amplifies those capabilities into reliable task execution. The engineering practice of designing and optimizing this infrastructure outside the model is **Harness Engineering**.\n\n\n\nANONYMOUS CHINESE X:\n### 这里有三个观察要点。首先，强化学习训练让模型学会何时以及如何使用工具，这样客户端就不再需要手动编写工具调用的编排逻辑。其次，模型自行决定何时进行搜索以及搜索什么内容，展现出真正的自主性。第三，它会根据搜索结果调整策略，并判断是否已掌握足够信息。有一个常见的误解值得澄清：**强化学习赋予模型的是决策策略，而非工具本身**。它教会模型何时调用工具、选择哪个工具、传递什么参数、在收到结果后是否继续以及如何将数十次或数百次调用串联成连贯的推理；这些“何时以及如何使用”的判断被写入模型的权重中。**工具及其执行由Agent框架或API内置功能提供**：`web_search`和`code_runner`的实现、代码沙箱以及发出调用并返回结果的基础设施都存在于模型之外。强化学习优化的是决策策略；它不会将搜索引擎或代码沙箱嵌入模型的权重中。因此，编排循环并没有消失；它从客户端转移到了服务器端，而决策制定则进入了模型[^ch1-2]。\n\n[^ch1-2]: 感谢读者asdlem通过GitHub Issue #30指出并澄清了这一点，即强化学习内化的是工具调用的决策策略，而非工具执行机制。详见https://github.com/bojieli/ai-agent-book/issues/30\n\nKimi K3在Agent任务中的显著优势是**长链式工具调用的稳定性**——它可以持续进行200 - 300次连续的工具调用，且始终保持连贯的推理，远远超过大多数模型开始出现性能下降时的几十次调用。K3针对长视野编程和Agent工作负载进行了优化，发布了两种变体：K3 Max（用于对话和Agent任务）和K3 Swarm Max（用于大规模并行处理）。作为开源模型，它在软件工程和Agent基准测试中与顶级闭源系统相当——这证明了强化学习可以赋予模型原生的Agent能力。\n\n#### 实验1 - 3★：GPT - 5.6原生深度研究能力\n\n第二个实验使用**OpenAI GPT - 5.6**来展示，在API级内置工具的支持下，先进模型如何在服务器端闭合“搜索 - 阅读 - 分析”的编排循环以进行深度研究。GPT - 5.6有三种变体——Sol（旗舰前沿模型）、Terra（日常工作的平衡模型）和Luna（快速、经济的轻量模型），所有变体都将工具调用决策原生地留给模型，因此客户端不需要自己的编排框架。一个便利的功能是**自由格式工具调用**。传统上，模型调用工具时必须将每个参数序列化为严格的JSON（一种结构化数据格式），很像用严格格式规则填写表格。自由格式工具调用（通过`type: \"custom\"`的工具在API中声明）允许模型直接将原始文本发送给工具（一段Python代码、一个SQL查询），完全避免JSON转义。值得强调的是，这是API参数格式的演进，而非模型架构的创新——客户端的工具调用循环（检测`tool_calls`→执行→返回结果）保持不变；只是参数从JSON字符串变为原始文本。GPT - 5.6还引入了一个详细程度参数（控制输出细节）和一个推理努力参数（调整推理深度；Sol增加了一个最大级别以实现最彻底的推理时间），让开发者可以根据任务的复杂性调整模型行为。\n\nGPT - 5.6与Responses API的**网络搜索和代码解释器**内置工具配合，提供了深度研究的核心机制：模型可以自主搜索网络获取实时信息并编写代码进行深入分析，实现“搜索→阅读→分析→再次搜索”的迭代研究过程。例如，面对“东盟10国首都之间的最短距离是多少？”这样的问题时，GPT - 5.6会自动搜索每个首都的地理坐标，然后编写Python代码计算所有首都对之间的大圆距离，最终确定最近的一对。同样，在“搜索比特币过去一个月的趋势并进行技术分析”这样的任务中，它可以从多个金融数据源获取实时价格数据，使用专业的技术分析库计算移动平均线、相对强弱指数（RSI）、指数平滑异同移动平均线（MACD）等技术指标，生成可视化图表并提供交易建议。\n\n更重要的是，GPT - 5.6在模型层面内化了OpenAI深度研究产品的设计理念，引入了**意图澄清过程**。给定一个研究请求，GPT - 5.6不会立即开始执行；它首先通过一系列问题澄清用户的真实意图。对于“搜索比特币过去一个月的趋势并进行技术分析”，它会首先问：“您偏好哪个数据源？您希望分析哪些技术指标？”这种交互式澄清让GPT - 5.6能够生成更精确且更符合用户实际需求的研究报告。\n\nGPT - 5.6是“模型即Agent”的成熟示例——Responses API的网络搜索、代码解释器等内置工具在服务器端闭环执行；编排循环从客户端转移到了API服务器，简化了客户端实现。模型仍然发出标准的工具调用；客户端只是不再需要自己构建“搜索 - 阅读 - 分析”的编排框架。它最值得注意的方面是意图澄清机制：模型不是立即执行任务，而是首先确认用户真正需要什么，然后制定研究策略。在执行开始之前就解决了“用户所说的”和“用户实际想要的”之间的差距。\n\n图1 - 4展示了“模型即Agent”范式下原生工具调用的完整架构，以及Kimi K3和GPT - 5.6在实际任务中的ReAct执行过程。\n\n![Figure 1 - 4: \"Model as Agent\" Architecture—Native Tool Calling](images/fig1 - 4.svg)\n\n## 框架工程：超越模型的竞争力\n\n到目前为止，你已经了解了Agent的核心工作原理：大语言模型在上下文的引导下运行ReAct循环，使用工具完成任务。上面的实验表明基本机制是有效的——但也暴露了它的脆弱性。模型可能会幻觉（发明不存在的工具或参数）、选错工具或无法从错误中恢复。从一个可行的演示到可靠的产品存在相当大的差距，而这些脆弱性正是框架工程要解决的问题。本章前半部分回答了Agent是什么；后半部分回答了Agent如何在生产环境中可靠运行。\n\n前面的部分确立了核心公式：**Agent = 大语言模型 + 上下文 + 工具**。它描述了Agent的**内部组成**：推理引擎、工作上下文和行动接口。框架工程为同一系统添加了第二个**实现层面**的视角：将大语言模型视为一个核心组件（模型），将围绕它构建的所有支持代码称为框架。这两个视角不是竞争关系；它们在不同的抽象层次上描述同一个系统。我们切换到更通用的词“模型”，因为框架工程的原理适用于任何能够推理和调用工具的模型，而不仅仅是特定种类的模型。框架的核心是原始公式中的“上下文 + 工具”，再加上三层防护：**约束**（Agent可以做和不可以做的事情）、**验证**（是否正确完成了事情）和**纠正**（出错时如何恢复）。\n\n展开为一个等式，完整的生产级组成是：\n\n> **Agent = 大语言模型 + [上下文 + 工具 + 约束 + 验证 + 纠正] = 模型 + 框架**\n\n一个最小的可用Agent仅依靠大语言模型、上下文和工具运行。要在长时间的生产工作负载中可靠运行，它还需要三层外部工程层——约束以防止过度扩展，验证以捕获错误，纠正以从失败中恢复。这些层不是事后添加的独立模块；它们是围绕“上下文 + 工具”的防护措施。换句话说：最小公式是演示视角，扩展公式是生产视角——后者完全包含前者并在其周围添加了安全网。\n\n一个例子可以明确边界：将退款政策嵌入上下文中属于**上下文**，而检查退款金额不超过订单总额属于**约束**。执行API调用属于**工具**，而在API超时后自动重试属于**纠正**。模型提供底层的理解和推理；框架引导、约束并放大这些能力以实现可靠的任务执行。在模型之外设计和优化这种基础设施的工程实践就是**框架工程**。\n\nANONYMOUS CHINESE Y:\n### 人工智能代理入门[第5/9部分]\n这里有三个观察要点。首先，强化学习训练让模型学习何时以及如何使用工具，所以客户端不再需要手动编写工具调用的编排逻辑。其次，模型自行决定何时搜索以及搜索什么，展现出真正的自主性。第三，它根据搜索结果调整策略，并判断是否有足够的信息。有一个常见误解值得澄清：**强化学习赋予模型的是决策策略**，而不是工具本身。它教会模型何时调用工具、选择哪个工具、传递什么参数、在收到结果后是否继续以及如何将数十或数百次调用链成连贯的推理；这些*何时以及如何使用*的判断被写入模型的权重中。**工具及其执行由代理框架或API内置提供**：`web_search`和`code_runner`的实现、代码沙盒以及发出调用和返回结果的基础设施都在模型之外。强化学习优化的是决策策略；它不会将搜索引擎或代码沙盒嵌入模型的权重中。因此，编排循环没有消失；它从客户端转移到了服务器端，而决策制定进入了模型[^ch1-2]。\n\n[^ch1-2]: 感谢读者asdlem通过GitHub问题#30指出并澄清了强化学习内化的是工具调用决策策略，而非工具执行机制这一区别。见https://github.com/bojieli/ai-agent-book/issues/30\n\nKimi K3在代理任务中的显著优势是**长链工具调用的稳定性**——它可以持续进行200-300次连续的工具调用，整个过程中推理连贯，远远超过大多数模型开始退化的几十次调用。K3针对长视距编程和代理工作负载进行了优化，发布了两个变体：K3 Max（用于对话和代理任务）和K3 Swarm Max（用于大规模并行处理）。作为开源模型，它在软件工程和代理基准测试中与顶级闭源系统相当——这证明强化学习可以赋予模型本地代理能力。\n\n#### 实验1-3 ★：GPT-5.6的本地深度研究能力\n第二个实验使用**OpenAI GPT-5.6**展示了一个由API级内置工具支持的先进模型如何在服务器端闭合“搜索-阅读-分析”的编排循环，用于深度研究。GPT-5.6有三个变体——Sol（旗舰前沿模型）、Terra（日常工作的平衡模型）和Luna（快速、经济的轻量模型）——都将工具调用决策本地留给模型，所以客户端不需要自己的编排框架。一个方便的功能是**自由形式工具调用**。传统上，模型调用工具必须将每个参数序列化为严格的JSON（一种结构化数据格式），非常像用严格格式规则填写表格。自由形式工具调用（通过`type: \"custom\"`的工具在API中声明）允许模型直接向工具发送原始文本（一段Python代码、一个SQL查询），完全避免JSON转义。值得强调的是，这是API参数格式的演进，而不是模型架构的创新——客户端的工具调用循环（检测`tool_calls`→执行→返回结果）保持不变；只是参数从JSON字符串变为原始文本。GPT-5.6还引入了一个详细程度参数（控制输出细节）和一个推理努力参数（调整推理深度；Sol添加了一个最大级别以实现最彻底的推理时间），让开发者可以根据任务的复杂性调整模型行为。\n\nGPT-5.6与Responses API的**网络搜索和代码解释器**内置工具配合，提供了深度研究的核心机制：模型可以自主搜索网络获取实时信息并编写代码进行深入分析，实现“搜索→阅读→分析→再次搜索”的迭代研究过程。例如，面对“东盟10国首都之间的最短距离是多少？”这样的问题，GPT-5.6会自动搜索每个首都的地理坐标，然后编写Python代码计算所有首都对之间的大圆距离，最终识别出最近的一对。同样，在“搜索比特币过去一个月的趋势并进行技术分析”这样的任务中，它可以从多个金融数据源获取实时价格数据，使用专业技术分析库计算移动平均线、相对强弱指数（RSI）、MACD等技术指标，生成可视化图表并提供交易建议。\n\n更重要的是，GPT-5.6在模型层面引入了**意图澄清过程**，内化了OpenAI深度研究产品的设计理念。给定一个研究请求，GPT-5.6不会立即开始执行；它首先通过一系列问题澄清用户的真实意图。对于“搜索比特币过去一个月的趋势并进行技术分析”，它会首先问：“您偏好哪个数据源？您希望分析哪些技术指标？”这种交互式澄清让GPT-5.6能够生成更精确且更符合用户实际需求的研究报告。\n\nGPT-5.6是“模型即代理”的成熟示例——网络搜索、代码解释器和Responses API的其他内置工具在服务器端闭环执行；编排循环从客户端转移到API服务器，简化了客户端实现。模型仍然发出标准的工具调用；客户端只是不再需要自己构建“搜索-阅读-分析”的编排框架。它最值得注意的方面是意图澄清机制：模型不是立即执行任务，而是首先确认用户真正需要什么，然后制定研究策略。在执行开始前就解决了“用户所说的”和“用户实际想要的”之间的差距。\n\n图1-4展示了“模型即代理”范式下本地工具调用的完整架构，以及Kimi K3和GPT-5.6在真实任务中的ReAct执行过程。\n\n![图1-4：“模型即代理”架构——本地工具调用](images/fig1-4.svg)\n\n## 框架工程：超越模型的竞争力\n到现在你已经了解了代理的核心工作原理：大语言模型在上下文的引导下运行ReAct循环，使用工具完成任务。上述实验表明基本机制是有效的——同时也暴露了它的脆弱性。模型可能会幻觉（发明不存在的工具或参数）、选择错误的工具或无法从错误中恢复。从工作演示到可靠产品存在巨大差距，而这些脆弱性正是框架工程要解决的。本章前半部分回答了代理是什么；后半部分回答了代理如何在生产中可靠运行。\n\n前面的章节建立了核心公式：**代理=大语言模型+上下文+工具**。它描述了代理的**内部组成**：推理引擎、工作上下文和行动接口。框架工程为同一系统添加了第二个**实现层面**的视图：将大语言模型视为一个核心组件（模型），将围绕它构建的所有支持代码称为框架。这两个视图不是竞争关系；它们在不同抽象层次上描述同一系统。我们切换到更通用的“模型”一词，因为框架工程的原则适用于任何能够推理和调用工具的模型，而不仅仅是特定种类。框架的核心是原始公式中的“上下文+工具”，加上三层保障：**约束**（代理可以做和不可以做的事情）、**验证**（它是否正确完成了事情）和**纠正**（当它没有正确完成时如何恢复）。\n\n展开为一个等式，完整的生产级组成是：\n\n> **代理=大语言模型+[上下文+工具+约束+验证+纠正]=模型+框架**\n\n一个最小的工作代理仅靠大语言模型、上下文和工具就能运行。要在长时间运行的生产工作负载中可靠运行，它还需要三层外部工程层——约束以防止越界，验证以捕获错误，纠正以从失败中恢复。这些层不是事后添加的独立模块；它们是围绕“上下文+工具”的保障措施。换句话说：最小公式是演示视图，展开的公式是生产视图——后者完全包含前者并在其周围添加安全网。\n\n一个例子可以阐明边界：将退款政策嵌入上下文中属于**上下文**，而检查退款金额不超过订单总额属于**约束**。执行API调用属于**工具**，而在API超时后自动重试属于**纠正**。模型提供底层的理解和推理；框架引导、约束并将这些能力放大为可靠的任务执行。在模型之外设计和优化这种基础设施的工程实践就是**框架工程**。"
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