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            "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:\nExperiment 2-7 is a small qualitative demonstration. To quantify the value and limits of this \"precompute and access directly\" approach, the author and collaborators evaluated it with a dedicated benchmark[^ch2-7]. This approach has a general name: **Context Distillation**. The Agent Status Bar is its most common form. The benchmark covered three types of tasks (counting, rule induction, state tracking), 11 models (from advanced APIs to a 2B model that can run on a laptop), and nearly 24,000 evaluations. The results are clear:\n\n- **For weak models, a precomputed status bar recovers accuracy**—the weakest models saw accuracy gains of 40 to 54 percentage points, and on these tasks a local 2B model even matched a frontier model that had no status bar.\n- **For strong models that already answer correctly, it improves efficiency**—the same status bar reduces the reasoning effort, latency, and cost per query by roughly an order of magnitude (reasoning tokens are cut by 80–90% or more).\n- The most fundamental change is: without a status bar, the reasoning effort per query **grows continuously** as the context lengthens; with a status bar, it becomes **essentially constant**—no matter how long the context gets, the model reads those few status entries directly. This is the quantified version of the heatmap from Experiment 2-7: originally, attention spreads thinner as N increases; after adding the status bar, it locks firmly onto those fixed entries.\n\n(As an aside, the status bar must be written as key-value pairs that can be located quickly, like `Clothes: 9 items (Pass 7, Defect 2)`, not as a paragraph of prose—the paper showed that writing the same status information in prose form yielded significantly worse results, because the model still has to read and parse the prose, essentially returning to the scanning problem.)\n\nHowever, **how the precomputation is performed matters greatly**. The most important takeaways from this work are three directly actionable lessons:\n\n**1. Maintain the status bar with code, not with an LLM.** It may seem natural to ask another LLM to read the history and summarize the status bar, but the experiment found that this performed poorly. A 20-line regular-expression function achieved ground-truth-level accuracy, whereas a frontier model that processed the full history in one batch produced many incorrect entries and reduced downstream accuracy below the no-status-bar baseline. Asking an LLM to summarize a long history in one pass merely moves the original context-scanning problem elsewhere. A viable alternative is to **use code whenever possible**; if an LLM is necessary, have it **extract items one by one and then aggregate them with code, rather than summarizing the entire history in a single pass**.\n\n**2. Before deleting the original context, confirm that the status bar covers all questions that might be asked.** The status bar is a **lossy projection** of the original context: it only precomputes the dimensions you *anticipate* will be relevant. If the status bar is sufficient, as it is for tasks such as counting and state tracking, the original records can be deleted and only the status bar retained, saving many tokens. Performance can deteriorate sharply, however, when a question asks for information the status bar was not designed to capture. In the paper's extreme test, the status bar stored only counts for \"pairwise combinations,\" while the question asked about \"triple intersections.\" Retaining only the status bar caused accuracy to collapse, with Claude falling from 100% to 7.6%. A plausible but incomplete status bar can therefore become a \"false authority\" that confidently misleads the model. In practice, treat a new type of question like **a change to a database table schema**: either add the corresponding field to the status bar first or retain both the status bar and the original context. Some tasks, such as multi-hop reasoning across long passages of prose, cannot be captured by a clean structured summary. For these tasks, the status bar may save tokens, but it should not be expected to improve accuracy.\n\n**3. Monitor the accuracy of the status bar as a first-line production metric.** The experiment produced a striking finding: **the model almost unconditionally trusts the status bar**. If it says \"called 3 times,\" the model accepts that value without checking or recalculating it. This trust makes the status bar effective, but it also allows errors to flow **directly** into the final answer. The system tolerates modest inaccuracies: the benefits are largely preserved when values are off by less than about 10%. Larger errors, however, can make an incorrect status bar worse than having none. This also connects to the **status bar poisoning** risk discussed earlier. Status information should come from reliable observations of the real world and never from data sources that can be externally contaminated; otherwise, the instrument will report the wrong state and lead the model astray.\n\n[^ch2-7]: Li, Bojie and Noah Shi. *Distill, Don't Retrieve: Inference-Time Context Distillation for LLM Agent Reasoning.* 2026. https://01.me/research/context-distillation\n\n(The following is optional advanced material from current research. It can be skipped on first reading without affecting your understanding of how to use the status bar; the preceding mechanisms, evidence, and three lessons are sufficient to guide practice.)\n\nThe two principles above—distilling implicit state and steering attention—explain why the status bar works. A deeper point is that the status bar can **feed the model information it could not have inferred on its own**[^ch2-5].\n\nWe often describe two ways to make a model stronger at test time: **reason longer** (generate a longer chain of thought) and **sample more** (sample multiple answers and select the best). Both paths share the same limitation: they operate only within the model's internal computation, using fixed weights and fixed context. They **cannot create information that was not already present in the context**; they can only rearrange existing information. Interaction provides a third path. The model produces an output, an external instrument observes its real-world effect, and that observation is written back into the context. The observation may contain information the model **cannot infer through reasoning alone**: whether code passed the test, whether a rendered button overflowed the page, or what system state resulted from an operation. These facts come from execution and measurement, not from the weights or the existing context. (This research also found that the yardstick used to measure improvement must itself be grounded in real observations. If a visual model that only inspects a screenshot is used to score, it may fail to detect the defects it just fixed, causing the loop to make no real progress.)\n\nThe Agent Status Bar is the most common application of this principle. The Harness acts as the instrument: it observes runtime state (how many calls were made, the current time, task progress, whether a tool reported an error), compresses those observations into a short segment, and writes them back into the context. The most valuable part of the status bar is often not information the model could have counted by scanning the transcript, but **external facts it could not infer**. The status bar turns an isolated reasoning task into one grounded in real-world observations. This also gives a design principle: the more the status bar draws from real observations, the more valuable it is. Conversely, if the status summary is fabricated or comes from a data source that can be contaminated, the instrument will report the wrong state and mislead the model (this corresponds to the status bar poisoning risk discussed earlier).\n\n[^ch2-5]: Li, Bojie and Noah Shi. *Interaction Scaling: Grounding the Third Axis of Test-Time Compute.* arXiv:2607.11598, 2026.\n\nSeen from this perspective, the Loop Engineering introduced at the end of Chapter 1's evolutionary arc, and developed further in Chapter 10 alongside multi-agent collaboration systems, turns this third axis of interaction into engineering practice. Each iteration makes real progress only when verification writes observations of the external world back into the context. Without that step, the model merely rearranges existing information. Thus, the claim that \"the verifier, not the model, is the bottleneck\" and the finding that the measuring instrument must be grounded in real observations express the same principle.\n\n### Composition of the Agent Status Bar\n\nBased on the theoretical foundation above, the Agent Status Bar includes the following types of information:\n\n**Task Planning**: When an Agent handles complex, multi-step tasks, the trajectory can become very long. The Agent tends to focus excessively on the current local sub-task, forgetting the user's original request, core constraints, and subsequent work. Placing a TODO list that breaks the task into clear steps at the end of the trajectory continually reminds the model of its current progress and future goals, helping align its actions with the overall plan.\n\n**Side-channel Information for Events**: Attach metadata to each event—precise time, geographic location, time interval since the last Agent reply, etc. Side-channel information refers to auxiliary information not transmitted in the main data channel but helpful for understanding the event. This information helps the model understand the temporal relationships and environmental context of events, enabling more contextually appropriate decisions.\n\n\n\nANONYMOUS CHINESE X:\n### 上下文工程[第12/17部分]\n\n实验2-7是一个小型定性演示。为了量化这种“预先计算并直接访问”方法的价值和局限性，作者及其合作者使用专门的基准测试对其进行了评估[^ch2-7]。这种方法有一个通用名称：**上下文蒸馏**。代理状态栏是其最常见的形式。该基准测试涵盖了三类任务（计数、规则归纳、状态跟踪）、11个模型（从高级API到可在笔记本电脑上运行的20亿参数模型），并进行了近24,000次评估。结果清晰明了：\n\n- **对于弱模型，预先计算的状态栏恢复了准确性**——最弱的模型的准确性提高了40到54个百分点，在这些任务上，本地的20亿参数模型甚至可以与没有状态栏的前沿模型相媲美。\n- **对于已经能正确回答问题的强模型，它提高了效率**——相同的状态栏将推理工作量、时延和每次查询的成本降低了大约一个数量级（推理词元减少了80-90%或更多）。\n- 最根本的变化是：没有状态栏时，每次查询的推理工作量会随着上下文长度的增加而**持续增长**；有状态栏时，它变得**基本恒定**——无论上下文有多长，模型都会直接读取那几个状态栏条目。这是实验2-7中热图的量化版本：原本，随着N的增加，注意力分布变得更稀疏；添加状态栏后，注意力牢固地锁定在那些固定条目上。\n\n（顺便说一下，状态栏必须写成可以快速定位的键值对形式，例如`Clothes: 9 items (Pass 7, Defect 2)`，而不是一段散文——论文表明，以散文形式编写相同的状态信息会产生明显更差的结果，因为模型仍然需要读取和解析散文，本质上又回到了扫描问题。）\n\n然而，**预先计算的执行方式非常重要**。这项工作的最重要收获是三个直接可行的经验教训：\n\n**1. 用代码维护状态栏，而不是用大语言模型**。要求另一个大语言模型读取历史并总结状态栏似乎很自然，但实验发现这种方法效果很差。一个20行的正则表达式函数就能达到真实值水平的准确性，而一次性处理完整历史的前沿模型却产生了许多错误条目，导致下游准确性低于没有状态栏的基线。要求大语言模型一次性总结长历史只是将原来的上下文扫描问题转移到了其他地方。可行的替代方法是**尽可能使用代码**；如果必须使用大语言模型，让它**逐个提取项目，然后用代码聚合它们，而不是一次性总结整个历史**。\n\n**2. 在删除原始上下文之前，确认状态栏涵盖了所有可能被问到的问题**。状态栏是原始上下文的**有损投影**：它只预先计算你**预期**相关的维度。如果状态栏足够，例如在计数和状态跟踪等任务中，就可以删除原始记录，只保留状态栏，从而节省许多词元。然而，当问题询问状态栏未设计捕获的信息时，性能可能会急剧下降。在论文的极端测试中，状态栏仅存储“成对组合”的计数，而问题却询问“三重交集”。仅保留状态栏会导致准确性崩溃，Claude从100%降至7.6%。因此，一个合理但不完整的状态栏可能成为“虚假权威”，自信地误导模型。实际上，对待新类型的问题就像**数据库表结构的更改**：要么先将相应字段添加到状态栏中，要么同时保留状态栏和原始上下文。有些任务，例如跨长段散文的多跳推理，无法通过简洁的结构化总结来捕获。对于这些任务，状态栏可能节省词元，但不应期望它提高准确性。\n\n**3. 将状态栏的准确性作为一线生产指标进行监控**。实验有一个惊人的发现：**模型几乎无条件地信任状态栏**。如果它说“调用了3次”，模型会接受该值而不检查或重新计算。这种信任使状态栏有效，但也会让错误直接流入最终答案。系统容忍适度的不准确性：当值偏差小于约10%时，大部分好处仍然保留。然而，较大的错误会使不正确的状态栏比没有状态栏更糟糕。这也与前面讨论的**状态栏中毒**风险相关。状态信息应来自对现实世界的可靠观察，绝不能来自可能被外部污染的数据源；否则，仪器会报告错误的状态并误导模型。\n\n[^ch2-7]: 李博杰和诺亚·石。《Distill, Don't Retrieve: Inference-Time Context Distillation for LLM Agent Reasoning》。2026年。https://01.me/research/context-distillation\n\n（以下是当前研究中的可选高级材料。首次阅读时可以跳过，不影响对状态栏使用方法的理解；前面的机制、证据和三个经验教训足以指导实践。）\n\n上述两个原则——提炼隐式状态和引导注意力——解释了状态栏起作用的原因。更深入的一点是，状态栏可以**为模型提供它自身无法推断出的信息**[^ch2-5]。\n\n我们通常描述两种在测试时增强模型能力的方法：**延长推理**（生成更长的思维链）和**增加采样**（采样多个答案并选择最佳答案）。这两条路径都有相同的局限性：它们仅在模型的内部计算中操作，使用固定的权重和固定的上下文。它们**无法创建上下文中不存在的信息**；只能重新排列现有信息。交互提供了第三条路径。模型产生输出，外部仪器观察其真实世界的效果，然后将该观察结果写回上下文中。观察结果可能包含模型**仅通过推理无法推断出的信息**：代码是否通过了测试、渲染的按钮是否溢出页面、操作产生了什么系统状态等。这些事实来自执行和测量，而不是权重或现有上下文。（这项研究还发现，衡量改进的标准本身必须基于真实观察。如果使用仅检查截图的视觉模型进行评分，它可能无法检测到刚刚修复的缺陷，导致循环无法取得真正的进展。）\n\n代理状态栏是这一原则最常见的应用。测试平台充当仪器：它观察运行时状态（调用了多少次、当前时间、任务进度、工具是否报告错误），将这些观察结果压缩成简短的片段，然后写回上下文中。状态栏最有价值的部分往往不是模型通过扫描记录可以计数的信息，而是**它无法推断出的外部事实**。状态栏将孤立的推理任务转变为基于真实世界观察的任务。这也给出了一个设计原则：状态栏从真实观察中获取的信息越多，其价值就越大。相反，如果状态总结是编造的或来自可能被污染的数据源，仪器会报告错误的状态并误导模型（这对应前面讨论的状态栏中毒风险）。\n\n[^ch2-5]: 李博杰和诺亚·石。《Interaction Scaling: Grounding the Third Axis of Test-Time Compute》。arXiv:2607.11598，2026年。\n\n从这个角度来看，第1章进化弧末尾介绍的循环工程，以及第10章与多代理协作系统一起进一步发展的循环工程，将这种交互的第三轴转化为工程实践。只有当验证将外部世界的观察结果写回上下文中时，每次迭代才能取得真正的进展。没有这一步骤，模型只是重新排列现有信息。因此，“验证器而不是模型是瓶颈”这一说法，以及测量仪器必须基于真实观察的发现，表达了相同的原则。\n\n### 代理状态栏的组成\n\n基于上述理论基础，代理状态栏包括以下类型的信息：\n\n**任务规划**：当代理处理复杂的多步骤任务时，轨迹可能会非常长。代理往往会过度关注当前的局部子任务，忘记用户的原始请求、核心约束和后续工作。在轨迹末尾放置一个将任务分解为清晰步骤的待办事项列表，不断提醒模型其当前进度和未来目标，有助于使其行动与整体计划保持一致。\n\n**事件的旁通信息**：为每个事件附加元数据——精确时间、地理位置、自上次代理回复以来的时间间隔等。旁通信息是指不在主要数据通道中传输但有助于理解事件的辅助信息。这些信息帮助模型理解事件的时间关系和环境背景，从而做出更符合上下文的决策。\n\nANONYMOUS CHINESE Y:\n### 上下文工程[第12/17部分]\n实验2-7是一个小型定性演示。为了量化这种“预先计算并直接访问”方法的价值和限制，作者及其合作者使用专门的基准进行了评估[^ch2-7]。这种方法有一个通用名称：**上下文提炼**。代理状态栏是其最常见的形式。基准涵盖了三种类型的任务（计数、规则归纳、状态跟踪）、11个模型（从高级API到可在笔记本电脑上运行的2B模型）和近24,000次评估。结果清晰明了：\n\n- **对于弱模型，预先计算的状态栏恢复了准确性**——最弱的模型准确率提高了40到54个百分点，在这些任务上，本地2B模型甚至与没有状态栏的前沿模型相当。\n- **对于已经正确回答的强模型，它提高了效率**——相同的状态栏将每次查询的推理工作量、延迟和成本降低了大约一个数量级（推理标记减少了80-90%或更多）。\n- 最根本的变化是：没有状态栏时，每次查询的推理工作量随着上下文长度的增加而**持续增长**；有状态栏时，它变得**基本恒定**——无论上下文有多长，模型直接读取那几个状态条目。这是实验2-7中热力图的量化版本：最初，随着N的增加，注意力分布变稀；添加状态栏后，它牢固地锁定在那些固定条目上。\n\n（顺便说一句，状态栏必须写成可以快速定位的键值对，例如`Clothes: 9 items (Pass 7, Defect 2)`，而不是一段散文——论文表明，以散文形式写入相同的状态信息会产生明显更差的结果，因为模型仍然必须读取和解析散文，基本上回到了扫描问题。）\n\n然而，**预先计算的执行方式非常重要**。这项工作的最重要收获是三个直接可行的经验：\n\n**1. 用代码维护状态栏，而不是用大语言模型**。要求另一个大语言模型读取历史并总结状态栏似乎很自然，但实验发现这种方法效果很差。一个20行的正则表达式函数达到了真实水平的准确率，而一次性处理完整历史的前沿模型产生了许多错误条目，使下游准确率低于无状态栏的基线。要求大语言模型一次性总结长历史只是将原始上下文扫描问题转移到了其他地方。可行的替代方法是**尽可能使用代码**；如果必须使用大语言模型，让它**逐个提取项目，然后用代码聚合它们，而不是一次性总结整个历史**。\n\n**2. 在删除原始上下文之前，确认状态栏涵盖了可能被问到的所有问题**。状态栏是原始上下文的**有损投影**：它仅预先计算你*预期*相关的维度。如果状态栏足够，例如在计数和状态跟踪等任务中，原始记录可以删除，仅保留状态栏，节省许多标记。然而，当问题询问状态栏未设计捕获的信息时，性能可能急剧下降。在论文的极端测试中，状态栏仅存储“成对组合”的计数，而问题询问“三重交集”。仅保留状态栏导致准确率崩溃，Claude从100%降至7.6%。因此，一个合理但不完整的状态栏可能成为“虚假权威”，自信地误导模型。在实践中，将新类型的问题视为**数据库表架构的更改**：要么首先将相应字段添加到状态栏，要么同时保留状态栏和原始上下文。某些任务，例如跨长段散文的多跳推理，无法通过清晰的结构化摘要捕获。对于这些任务，状态栏可能节省标记，但不应期望它提高准确率。\n\n**3. 将状态栏的准确率作为一线生产指标进行监控**。实验有一个惊人的发现：**模型几乎无条件信任状态栏**。如果它说“拨打了3次”，模型会接受该值而不检查或重新计算。这种信任使状态栏有效，但也允许错误**直接**流入最终答案。系统容忍适度的不准确性：当值偏差小于约10%时，好处基本保留。然而，更大的错误可能使不正确的状态栏比没有状态栏更糟。这也与前面讨论的**状态栏中毒**风险相关。状态信息应来自对现实世界的可靠观察，永远不应来自可能被外部污染的数据源；否则，仪器将报告错误的状态并误导模型。\n\n[^ch2-7]: Li, Bojie and Noah Shi. *Distill, Don't Retrieve: Inference-Time Context Distillation for LLM Agent Reasoning.* 2026. https://01.me/research/context-distillation\n\n（以下是当前研究的可选高级材料。首次阅读时可以跳过，不影响对状态栏使用的理解；前面的机制、证据和三个经验足以指导实践。）\n\n上面提到的两个原则——提炼隐含状态和引导注意力——解释了状态栏起作用的原因。更深入的一点是，状态栏可以**向模型提供它自己无法推断的信息**[^ch2-5]。\n\n我们经常描述两种在测试时增强模型的方法：**延长推理**（生成更长的思维链）和**增加采样**（采样多个答案并选择最佳答案）。这两种路径都有相同的限制：它们仅在模型的内部计算中操作，使用固定权重和固定上下文。它们**无法创建上下文中不存在的信息**；它们只能重新排列现有信息。交互提供了第三条路径。模型生成输出，外部仪器观察其真实世界的效果，然后将该观察写回上下文中。观察可能包含模型**仅通过推理无法推断的信息**：代码是否通过了测试、呈现的按钮是否溢出页面、操作导致的系统状态是什么。这些事实来自执行和测量，而不是来自权重或现有上下文。（这项研究还发现，用于衡量改进的标准本身必须基于真实观察。如果使用仅检查截图的视觉模型进行评分，它可能无法检测到刚刚修复的缺陷，导致循环没有真正进展。）\n\n代理状态栏是该原则最常见的应用。框架充当仪器：它观察运行时状态（进行了多少次调用、当前时间、任务进度、工具是否报告错误），将这些观察压缩成短段，然后写回上下文中。状态栏最有价值的部分往往不是模型通过扫描记录可以计数的信息，而是**它无法推断的外部事实**。状态栏将孤立的推理任务转变为基于现实观察的任务。这也给出了一个设计原则：状态栏从真实观察中汲取的信息越多，它就越有价值。相反，如果状态总结是伪造的或来自可能被污染的数据源，仪器将报告错误的状态并误导模型（这对应前面讨论的状态栏中毒风险）。\n\n[^ch2-5]: Li, Bojie and Noah Shi. *Interaction Scaling: Grounding the Third Axis of Test-Time Compute.* arXiv:2607.11598, 2026.\n\n从这个角度看，第1章演进弧末尾介绍的循环工程，以及第10章与多代理协作系统一起进一步发展的循环工程，将这种第三条交互轴转化为工程实践。只有当验证将外部世界的观察写回上下文中时，每次迭代才会取得真正的进展。没有该步骤，模型仅重新排列现有信息。因此，“验证者而不是模型是瓶颈”的主张，以及测量仪器必须基于真实观察的发现，表达了相同的原则。\n\n### 代理状态栏的组成\n基于上述理论基础，代理状态栏包括以下类型的信息：\n\n**任务规划**：当代理处理复杂的多步任务时，轨迹可能变得非常长。代理往往过度关注当前局部子任务，忘记用户的原始请求、核心约束和后续工作。在轨迹末尾放置一个将任务分解为清晰步骤的待办事项列表，不断提醒模型其当前进度和未来目标，帮助使其行动与整体计划保持一致。\n\n**事件的旁道信息**：为每个事件附加元数据——精确时间、地理位置、自代理上一次回复以来的时间间隔等。旁道信息是指未在主要数据通道中传输但有助于理解事件的辅助信息。此信息帮助模型理解事件的时间关系和环境上下文，从而做出更符合上下文的决策。"
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