ai-agent-book/chapter10/book-translation/validation/real_20260730T061500Z_v4/judge_receipts/unit-20.json

{
  "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:\nA common misconception needs clarification: \"KV Cache-friendly\" does not mean \"zero cost.\" The first insertion of those few hundred to few thousand tokens still incurs a write cost (as mentioned earlier, Prompt Cache writes may even be billed at a premium). The precise meaning is **write once, benefit repeatedly**: to make the model aware of a Skill's existence or a piece of document content, that information must enter the cache at least once. Claude Code pays this cost only once, with no repetition for the rest of the session. Compare this with placing the same information into the system prompt: every update invalidates the downstream trajectory and forces cache creation again, often for tens or hundreds of thousands of tokens. That is the truly cache-unfriendly case.\n\n### Relationship Between Skills and Tools\n\nFrom a context-management perspective, the Skills mechanism is highly KV Cache-friendly. If all specialized code-tool definitions were placed in the system prompt, their proliferation would consume many tokens, and every change would invalidate the cached prefix. Under the Skill + generic executor model, however, the tool set remains small—as Chapter 5 shows, only seven core tools are required—and Skill content is loaded on demand through the progressive-disclosure mechanism described above, without affecting the cached prefix. Chapter 4 provides a detailed comparison and selection framework for these two forms, while Chapter 8 examines how an Agent undergoing continuous evolution decides whether an experience should be encoded as knowledge, instructions, a program, or model parameters.\n\n> **Experiment 2-6 ★★: Generate a Presentation from a Paper Using Agent Skills**\n>\n> **Experiment Goal**: Verify the Agent's ability to complete complex tasks by dynamically loading specialized domain Skills.\n>\n> Use Claude Code + PPTX Skill to generate a 10–15 slide presentation from a PDF of an academic paper. The Agent's execution flow demonstrates the progressive loading process:\n>\n> 1. Sees the PPTX Skill description in the Skill metadata list at the end of the context\n> 2. Identifies that the task requires this Skill\n> 3. Loads the complete `SKILL.md` via the Skill tool to obtain the core workflow\n> 4. Selectively loads `html2pptx.md` for detailed methods\n> 5. Uses bundled tool scripts (e.g., `scripts/thumbnail.py`) for preview generation, and template files as a design starting point\n>\n> **Acceptance Criteria**: The generated PowerPoint covers the paper's main content (title page, problem background, method overview, key results, conclusion), includes at least 3 figures extracted from the paper that are consistent with the text descriptions, and has correct formatting that opens properly in PowerPoint or compatible software.\n>\n\n## Agent Status Bar: Managing Trajectories with Meta-Information\n\n![Figure 2-14: Agent Status Bar Architecture](images/fig2-14.svg)\n\nThe Skills section introduced the \"user-role meta message at the end of the context\" as a general channel for injecting meta-information. The Skill metadata list is one use of that channel. This section develops the mechanism more systematically: the Agent framework can use it to synchronize dynamic runtime state with the model. This mechanism is called the **Agent Status Bar**.\n\nThe prompt engineering discussed earlier solved the problem of \"what static instructions to give the model.\" However, during actual execution, the Agent also needs to track its own state and task progress dynamically—this is where the Agent Status Bar comes in.\n\nWhen building production-grade Agent systems, relying solely on the native capabilities of LLMs is often insufficient. Agents executing complex tasks can fall into failure modes such as infinite loops, loss of state, and goal drift. The root cause is often that the model lacks a clear view of the current environment state and task progress. The Agent Status Bar addresses this by embedding structured meta-information in the context, giving the model explicit state signals it can use during decision-making.\n\nThe closest analogy is the **status bar** of an operating system. On a phone, the top of the screen displays the time, battery level, signal strength, and notification count. This information is not the main content of the app, but it gives users immediate access to the device's current state. The Agent Status Bar serves a similar purpose for the model: it is not part of the conversation's primary content—not an end-user request, model output, or tool result—but a **state summary** injected by the Agent framework at the end of the context: \"You have made 3 calls,\" \"Current time is 10:30,\" \"2 TODO items remaining.\" Each time the model generates a response, it can use this state to make better decisions.\n\nThe distinction from the System Prompt is clear: the System Prompt is the fixed operating manual, while the Agent Status Bar is a real-time dashboard continuously updated as the task progresses.\n\n### Theoretical Basis of the Agent Status Bar\n\nThe effectiveness of the Agent Status Bar stems from a fundamental property of the attention mechanism: in-context learning is more retrieval-like than reasoning-like. The model is good at finding information that already exists in the context, but less reliable at actively summarizing that context and deriving aggregate state during a single forward pass. This refers to how the model consumes existing context in one forward pass; it does not negate the model's ability to perform multi-step reasoning through chain-of-thought generation.\n\nPut differently, attention gives the model strong retrieval-like access to existing tokens. Given a question, it can often pull relevant raw records out of thousands of tokens, making every forward pass resemble a lightweight form of Retrieval-Augmented Generation (RAG). What is missing is an automatic **distillation layer**. The context is not automatically counted, indexed, or summarized in place. Any conclusion *about* the content—how many items there are, whether a limit has been exceeded, how far along the task is—must be recomputed from the raw records when the model needs it. The cost of that recomputation rises with the amount of content accumulated in the context.\n\nConsider a real-world scenario: an Agent needs to make phone calls to complete business tasks, and the system prompt requires calling each merchant no more than three times. But after calling three times, the Agent often miscounts how many times it has called, makes a fourth call, or even falls into a loop repeatedly calling the same number.\n\nThe problem is that the answer to \"How many times have I called?\" is not automatically distilled into an explicit fact. Instead, it remains scattered across raw call records in the KV Cache. Each time the model makes a decision, it must spend extra reasoning tokens to scan the context and recount, a process that is highly inefficient and error-prone.\n\nWhen we directly include the repeat call count in the tool call result for each phone call (e.g., \"This is the third call to this merchant\"), the model can immediately recognize that the limit has been reached and stop calling, significantly reducing error rates.\n\nThe essence of this mechanism is **distilling implicit states scattered throughout the context into explicit knowledge that can be directly used**. Information in the raw trajectory is highly redundant—a large number of tokens contain only a small amount of key state information. The Agent Status Bar actively extracts these key states, presenting—at minimal additional token cost—information that would otherwise require scanning thousands of tokens.\n\nIn long-context scenarios, the model's attention resources are limited. As context length increases, the model must allocate attention across more candidate content, so key information may receive insufficient weight. In complex Agent trajectories, task goals and early constraints can be overwhelmed by later tool results. The model also tends to over-focus on recent context, creating \"attention decay\" for information located in the middle of the context.\n\nThe Agent Status Bar addresses this problem by deliberately placing key meta-information in a structured format at the end of the context. Because this information is close to the tokens the model is about to generate, it is more likely to receive attention. This is a form of attention steering through placement.\n\n> **Experiment 2-7 ★★: Verifying the Effect of the Agent Status Bar via Attention Visualization**\n>\n> Based on the `attention_visualization` project, we designed a controlled experiment where a customer service Agent handles a refund request. The Agent has already called Xfinity 3 times, interspersed with web searches. The user asks: \"Can you call them again to follow up?\"\n>\n> **Control Group A (No Status Bar):** The context contains the complete trajectory but no aggregated status information. The heatmap shows widely dispersed attention, with distinct concentrations around the three phone-call records. The reasoning tokens show the model counting and tallying information from the raw records.\n>\n> **Control Group B (With Status Bar):** The following is appended at the end of the trajectory:\n>\n> ```xml\n> <agent_status>\n> Current State:\n> - Tool call summary: 'phone_call' has been invoked 3 times (Xfinity: 3 times)\n> - Constraint check: Maximum calls to Xfinity reached (3/3)\n> </agent_status>\n> ```\n>\n> Attention is highly concentrated on the status bar information. The reasoning process directly uses the already distilled information, no longer computing statistics from the raw data. For a small model like Qwen3-0.6B, Control Group A frequently violates the constraint and continues calling, while Control Group B consistently adheres to the constraint.\n>\n\n\n\nANONYMOUS CHINESE X:\n### 上下文工程[第11/17部分]\n一个常见的误解需要澄清：“对KV缓存友好”并不意味着“零成本”。最初插入的几百到几千个标记仍然会产生写入成本（如前所述，提示缓存写入甚至可能收取溢价）。确切的含义是**写入一次，反复受益**：为了让模型了解技能的存在或文档内容的一部分，该信息必须至少进入缓存一次。Claude Code仅支付一次此成本，会话其余部分不再重复。将相同信息放入系统提示进行比较：每次更新都会使下游轨迹失效并强制再次创建缓存，通常涉及数十万甚至数百万个标记。这才是真正对缓存不友好的情况。\n\n### 技能与工具的关系\n从上下文管理的角度来看，技能机制对KV缓存非常友好。如果所有专门的代码工具定义都放在系统提示中，它们的大量增加会消耗许多标记，每次更改都会使缓存的前缀失效。然而，在技能+通用执行器模型下，工具集保持较小——如第5章所示，只需要七个核心工具——技能内容通过上述渐进披露机制按需加载，不影响缓存的前缀。第4章提供了这两种形式的详细比较和选择框架，第8章探讨了持续演进的代理如何决定经验应编码为知识、指令、程序还是模型参数。\n\n> **实验2-6 ★★：使用代理技能从论文生成演示文稿**\n>\n> **实验目标**：验证代理通过动态加载专业领域技能完成复杂任务的能力。\n>\n> 使用Claude Code + PPTX技能从学术论文的PDF生成10-15页的演示文稿。代理的执行流程展示了渐进加载过程：\n>\n> 1. 在上下文末尾的技能元数据列表中看到PPTX技能描述\n> 2. 识别出任务需要此技能\n> 3. 通过技能工具加载完整的`SKILL.md`以获取核心工作流\n> 4. 有选择地加载`html2pptx.md`以获取详细方法\n> 5. 使用捆绑的工具脚本（例如`scripts/thumbnail.py`）生成预览，并使用模板文件作为设计起点\n>\n> **验收标准**：生成的PowerPoint涵盖论文的主要内容（标题页、问题背景、方法概述、关键结果、结论），包含至少3个从论文中提取的与文本描述一致的图表，并且格式正确，可在PowerPoint或兼容软件中正常打开。\n\n## 代理状态栏：用元信息管理轨迹\n![图2-14：代理状态栏架构](images/fig2-14.svg)\n\n技能部分介绍了“上下文末尾的用户角色元消息”作为注入元信息的通用通道。技能元数据列表是该通道的一种用途。本节更系统地发展该机制：代理框架可以使用它与模型同步动态运行时状态。这种机制称为**代理状态栏**。\n\n前面讨论的提示工程解决了“给模型的静态指令是什么”的问题。然而，在实际执行中，代理还需要动态跟踪自身状态和任务进度——这就是代理状态栏发挥作用的地方。\n\n构建生产级代理系统时，仅依赖大语言模型的原生能力往往不足。执行复杂任务的代理可能陷入无限循环、状态丢失和目标漂移等失败模式。根本原因通常是模型缺乏对当前环境状态和任务进度的清晰视图。代理状态栏通过在上下文中嵌入结构化元信息来解决这个问题，为模型提供决策时可使用的明确状态信号。\n\n最接近的类比是操作系统的**状态栏**。在手机上，屏幕顶部显示时间、电池电量、信号强度和通知计数。这些信息不是应用的主要内容，但让用户立即访问设备的当前状态。代理状态栏对模型起到类似的作用：它不是对话的主要内容——不是最终用户请求、模型输出或工具结果——而是代理框架在上下文末尾注入的**状态摘要**：“你已经进行了3次调用”、“当前时间是10:30”、“剩余2个待办事项”。每次模型生成响应时，它可以使用此状态做出更好的决策。\n\n与系统提示的区别很明显：系统提示是固定的操作手册，而代理状态栏是任务进展中持续更新的实时仪表盘。\n\n### 代理状态栏的理论基础\n代理状态栏的有效性源于注意力机制的一个基本属性：上下文学习更类似于检索而不是推理。模型擅长找到上下文中已存在的信息，但在单次前向传递中主动总结该上下文并推导聚合状态时可靠性较低。这指的是模型在一次前向传递中消耗现有上下文的方式；它并不否定模型通过思维链生成进行多步推理的能力。\n\n换句话说，注意力让模型对现有标记具有很强的检索式访问能力。给定一个问题，它通常可以从数千个标记中提取相关的原始记录，使每次前向传递类似于轻量级的检索增强生成（RAG）。缺少的是自动**提炼层**。上下文不会自动计数、索引或就地总结。任何关于内容的结论——有多少项、是否超过限制、任务进展到什么程度——都必须在模型需要时从原始记录中重新计算。这种重新计算的成本随着上下文中积累的内容量而增加。\n\n考虑一个现实场景：代理需要打电话完成业务任务，系统提示要求每个商家最多拨打三次。但拨打三次后，代理经常错误计数拨打次数，进行第四次拨打，甚至陷入反复拨打同一号码的循环。\n\n问题在于，“我拨打了多少次？”的答案不会自动提炼为明确的事实。相反，它仍然分散在KV缓存中的原始呼叫记录中。每次模型做出决策时，都必须花费额外的推理标记来扫描上下文并重新计数，这个过程效率极低且容易出错。\n\n当我们在每次电话呼叫的工具调用结果中直接包含重复呼叫计数（例如“这是对该商家的第三次呼叫”），模型可以立即识别出已达到限制并停止呼叫，显著降低错误率。\n\n该机制的本质是**将分散在上下文中的隐含状态提炼为可直接使用的明确知识**。原始轨迹中的信息高度冗余——大量标记仅包含少量关键状态信息。代理状态栏主动提取这些关键状态，以最小的额外标记成本呈现否则需要扫描数千个标记的信息。\n\n在长上下文场景中，模型的注意力资源有限。随着上下文长度增加，模型必须在更多候选内容之间分配注意力，因此关键信息可能获得的权重不足。在复杂的代理轨迹中，任务目标和早期约束可能被后期工具结果淹没。模型还倾向于过度关注最近的上下文，导致位于上下文中间的信息出现“注意力衰减”。\n\n代理状态栏通过故意将关键元信息以结构化格式放置在上下文末尾来解决这个问题。由于此信息靠近模型即将生成的标记，它更有可能获得注意力。这是一种通过放置进行注意力引导的形式。\n\n> **实验2-7 ★★：通过注意力可视化验证代理状态栏的效果**\n>\n> 基于`attention_visualization`项目，我们设计了一个对照实验，其中客户服务代理处理退款请求。代理已经拨打Xfinity 3次，中间穿插了网络搜索。用户问：“你能再打电话跟进吗？”\n>\n> **对照组A（无状态栏）**：上下文中包含完整轨迹但没有聚合状态信息。热力图显示注意力分散，在三个电话记录周围有明显的集中。推理标记显示模型从原始记录中计数和统计信息。\n>\n> **对照组B（有状态栏）**：在轨迹末尾附加以下内容：\n>\n> ```xml\n> <agent_status>\n> 当前状态：\n> - 工具调用摘要：'phone_call'已调用3次（Xfinity：3次）\n> - 约束检查：对Xfinity的最大呼叫次数已达到（3/3）\n> </agent_status>\n> ```\n>\n> 注意力高度集中在状态栏信息上。推理过程直接使用已提炼的信息，不再从原始数据计算统计信息。对于Qwen3-0.6B这样的小模型，对照组A经常违反约束并继续呼叫，而对照组B始终遵守约束。\n\nANONYMOUS CHINESE Y:\n### 上下文工程[第11/17部分]\n\n一个常见的误解需要澄清：“KV缓存友好”并不意味着“零成本”。最初插入那几百到几千个词元仍然会产生写入成本（如前所述，提示缓存写入甚至可能按溢价计费）。其确切含义是**写入一次，重复受益**：要让模型知道某项技能的存在或文档内容的一部分，该信息必须至少进入缓存一次。Claude Code只需支付一次此成本，在会话的其余部分无需重复支付。将相同信息放入系统提示与之对比：每次更新都会使下游轨迹失效，并强制再次创建缓存，通常涉及数十万计的词元。这才是真正不友好于缓存的情况。\n\n#### 技能与工具的关系\n\n从上下文管理的角度来看，技能机制非常友好于KV缓存。如果所有专门的代码-工具定义都放在系统提示中，它们的大量增加会消耗大量词元，并且每次更改都会使缓存前缀失效。然而，在技能+通用执行器模型下，工具集保持很小——如第5章所示，只需要七个核心工具——并且技能内容通过上述渐进披露机制按需加载，不影响缓存前缀。第4章提供了这两种形式的详细比较和选择框架，而第8章探讨了持续进化的智能体如何决定将经验编码为知识、指令、程序还是模型参数。\n\n> **实验2-6 ★★：使用智能体技能从论文生成演示文稿**\n>\n> **实验目标**：验证智能体通过动态加载专门领域技能完成复杂任务的能力。\n>\n> 使用Claude Code + PPTX技能从学术论文的PDF生成10–15页的演示文稿。智能体的执行流程展示了渐进加载过程：\n>\n> 1. 在上下文末尾的技能元数据列表中看到PPTX技能描述\n> 2. 识别出任务需要此技能\n> 3. 通过技能工具加载完整的`SKILL.md`以获取核心工作流\n> 4. 有选择地加载`html2pptx.md`以获取详细方法\n> 5. 使用捆绑的工具脚本（例如`scripts/thumbnail.py`）生成预览，并使用模板文件作为设计起点\n>\n> **验收标准**：生成的PowerPoint涵盖论文的主要内容（标题页、问题背景、方法概述、关键结果、结论），包含至少3张从论文中提取的与文本描述一致的图表，并且格式正确，可在PowerPoint或兼容软件中正常打开。\n\n## 智能体状态栏：用元信息管理轨迹\n\n![图2-14：智能体状态栏架构](images/fig2-14.svg)\n\n技能部分介绍了“上下文末尾的用户角色元消息”作为注入元信息的通用通道。技能元数据列表是该通道的一种用途。本节更系统地展开该机制：智能体框架可以用它来与模型同步动态运行时状态。该机制称为**智能体状态栏**。\n\n前面讨论的提示工程解决了“给模型静态指令是什么”的问题。然而，在实际执行中，智能体还需要动态跟踪自身状态和任务进展——这就是智能体状态栏发挥作用的地方。\n\n在构建生产级智能体系统时，仅依赖大语言模型的原生能力往往不够。执行复杂任务的智能体可能陷入无限循环、状态丢失、目标漂移等失败模式。根本原因通常是模型缺乏对当前环境状态和任务进展的清晰视图。智能体状态栏通过在上下文中嵌入结构化元信息来解决这个问题，为模型在决策时提供明确的状态信号。\n\n最接近的类比是操作系统的**状态栏**。在手机上，屏幕顶部显示时间、电池电量、信号强度和通知计数。这些信息不是应用的主要内容，但让用户立即了解设备的当前状态。智能体状态栏对模型起到类似作用：它不是对话的主要内容——不是最终用户请求、模型输出或工具结果——而是智能体框架在上下文末尾注入的**状态摘要**：“你已进行了3次通话”“当前时间是10:30”“还剩2个待办事项”。每次模型生成响应时，都可以利用此状态做出更好的决策。\n\n与系统提示的区别很明显：系统提示是固定的操作手册，而智能体状态栏是随着任务进展持续更新的实时仪表盘。\n\n### 智能体状态栏的理论基础\n\n智能体状态栏的有效性源于注意力机制的一个基本特性：上下文学习更类似于检索而非推理。模型擅长找到上下文中已存在的信息，但在单次前向传递中主动总结该上下文并推导聚合状态的可靠性较低。这指的是模型在一次前向传递中消耗现有上下文的方式；它并不否定模型通过思维链生成进行多步推理的能力。\n\n换句话说，注意力为模型提供了对现有词元的强大检索式访问。给定一个问题，它通常可以从数千个词元中提取相关的原始记录，使每次前向传递类似于轻量级的检索增强生成（RAG）形式。缺少的是自动的**提炼层**。上下文不会自动被计数、索引或就地总结。任何关于内容的结论——有多少项、是否超过限制、任务进展到哪一步——都必须在模型需要时从原始记录中重新计算。这种重新计算的成本随着上下文中积累的内容量而增加。\n\n考虑一个现实场景：智能体需要打电话完成业务任务，系统提示要求给每个商家打电话不超过三次。但在打了三次后，智能体经常错误计数已打电话次数，进行第四次通话，甚至反复陷入循环拨打同一号码。\n\n问题在于，“我打了多少次电话？”的答案没有自动提炼为明确的事实。相反，它仍然分散在KV缓存中的原始通话记录中。每次模型做出决策时，都必须花费额外的推理词元来扫描上下文并重新计数，这个过程效率极低且容易出错。\n\n当我们在每次电话通话的工具调用结果中直接包含重复通话计数（例如“这是给这个商家的第三次通话”），模型可以立即识别出已达到限制并停止通话，显著降低错误率。\n\n该机制的本质是**将分散在上下文中的隐式状态提炼为可直接使用的显式知识**。原始轨迹中的信息高度冗余——大量词元只包含少量关键状态信息。智能体状态栏主动提取这些关键状态，以最小的额外词元成本呈现否则需要扫描数千个词元才能获取的信息。\n\n在长上下文场景中，模型的注意力资源有限。随着上下文长度增加，模型必须在更多候选内容之间分配注意力，因此关键信息可能获得的权重不足。在复杂的智能体轨迹中，任务目标和早期约束可能被后续工具结果淹没。模型也往往过度关注近期上下文，导致位于上下文中间的信息出现“注意力衰减”。\n\n智能体状态栏通过故意将关键元信息以结构化格式放置在上下文末尾来解决这个问题。由于此信息靠近模型即将生成的词元，更有可能获得注意力。这是通过放置实现的注意力引导形式。\n\n> **实验2-7 ★★：通过注意力可视化验证智能体状态栏的效果**\n>\n> 基于`attention_visualization`项目，我们设计了一个对照实验，其中客服智能体处理退款请求。智能体已经给Xfinity打了3次电话，中间穿插了网络搜索。用户问：“你能再给他们打个电话跟进吗？”\n>\n> **对照组A（无状态栏）**：上下文中包含完整轨迹但没有聚合状态信息。热力图显示注意力分散，在三个电话记录周围有明显集中。推理词元显示模型从原始记录中计数和统计信息。\n>\n> **对照组B（有状态栏）**：在轨迹末尾附加以下内容：\n>\n> ```xml\n> <agent_status>\n> 当前状态：\n> - 工具调用摘要：'phone_call'已调用3次（Xfinity：3次）\n> - 约束检查：给Xfinity的最大通话次数已达（3/3）\n> </agent_status>\n> ```\n>\n> 注意力高度集中在状态栏信息上。推理过程直接使用已提炼的信息，不再从原始数据计算统计量。对于Qwen3-0.6B这样的小模型，对照组A经常违反约束继续拨打，而对照组B始终遵守约束。"
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        "model": "doubao-seed-1-6-250615",
        "created": 1785390905,
        "content": "{\"variants\":{\"X\":{\"accuracy\":{\"score\":5,\"evidence\":\"No omissions, inventions, or changed claims. For example, '写入一次，反复受益' accurately translates 'write once, benefit repeatedly'; '技能机制对KV缓存非常友好' correctly reflects 'the Skills mechanism is highly KV Cache-friendly'.\"},\"fluency\":{\"score\":4,\"evidence\":\"Most sentences are smooth, e.g., '这才是真正对缓存不友好的情况' flows naturally. Minor awkwardness: '技能内容通过上述渐进披露机制按需加载，不影响缓存的前缀' could be more polished, but still understandable.\"},\"terminology\":{\"score\":4,\"evidence\":\"Consistent use of '技能' (Skill), 'KV缓存' (KV Cache), '系统提示' (System Prompt). Inconsistency: '代理' vs. '智能体' (both translate 'Agent'; X uses '代理' consistently, which is acceptable but Y uses '智能体' consistently as well).\"},\"markdown_code_fidelity\":{\"score\":5,\"evidence\":\"Headings (###, ##), figures (![图2-14...]), blockquotes (>), code fences (```xml), and experiment formatting are all preserved correctly.\"}},\"Y\":{\"accuracy\":{\"score\":5,\"evidence\":\"All key claims are retained, e.g., '写入一次，重复受益' matches 'write once, benefit repeatedly'; '技能+通用执行器模型下，工具集保持很小' accurately reflects the source.\"},\"fluency\":{\"score\":5,\"evidence\":\"Smoother phrasing overall, e.g., '这才是真正不友好于缓存的情况' is more natural than X's version. '智能体状态栏通过故意将关键元信息以结构化格式放置在上下文末尾来解决这个问题' is highly readable.\"},\"terminology\":{\"score\":5,\"evidence\":\"Strictly consistent use of '智能体' (Agent), '词元' (token), '技能' (Skill), '工具集' (tool set). No internal inconsistencies; technical terms like '注意力衰减' (attention decay) are correctly translated.\"},\"markdown_code_fidelity\":{\"score\":5,\"evidence\":\"Markdown elements are fully preserved: headings, figure links, blockquotes, code blocks (e.g., the XML snippet in Experiment 2-7), and experiment structure are identical to the source.\"}}},\"preferred\":\"Y\",\"preference_evidence\":\"Y has higher fluency with more natural phrasing (e.g., '不友好于缓存' vs. X's '对缓存不友好') and stricter terminology consistency (consistently using '智能体' for 'Agent' instead of X's '代理'). Both have perfect accuracy and markdown fidelity, but Y's superior fluency and terminology consistency make it preferable.\"}",
        "usage": {
          "prompt_tokens": 5838,
          "completion_tokens": 585,
          "total_tokens": 6423
        }
      },
      "latency_milliseconds": 15547,
      "validation": {
        "valid": true
      }
    }
  ]
}