小节吗？等一下，原文的结构是连续的段落，但用户说长文用
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划分小节。先看原文的内容逻辑： 第一段：开头讲数值随种子和提示词变化，提出问题。然后讲噪声缩小相关性，教科书修正的问题，然后是自己的证明。然后讲提出的规则，效果。 接下来一段：提示词设置反转排名的例子，然后四个基准的平均结果。 然后一段：模型比较的方法，嵌入对齐的效果。 等一下，原文的自然段落其实是连续的，但用户说每个自然段落用
包裹，长文用
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划分小节。那先看原文的内容，可能可以分成几个小节：比如“Measurement Instability and Correction”相关的，“Prompt-Driven Ranking Reversals”，“Model Comparison and Embedding Alignment”？不过等一下，原文没有标题，所以不能自己加标题，因为规则说不得增删改内容。哦，规则4是保留原文所有信息，不得增删改内容，所以不能加标题，只能用原文的内容，用
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划分小节吗？等一下，规则2说“长文用
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划分小节”，但原文没有小节标题，所以可能需要看内容的逻辑结构，比如： 原文第一部分讲测量值的变化、修正方法的问题、提出的规则：这可以作为一个小节吗？但原文没有标题，所以可能不需要
，因为没有标题。或者原文的结构是多个段落，每个自然段落用
，但可能需要看内容的逻辑块。等一下，原文的内容是： 1. 开头：数值随种子和提示词变化，提出何时修正或比较的问题。然后讲噪声缩小相关性，教科书修正的问题，证明均方误差无限，提出的规则及其效果。 2. 然后：提示词设置反转排名的例子，四个基准的平均结果。 3. 然后：模型比较的方法，嵌入对齐的效果。 但原文是一个连续的文本，所以可能需要分成多个
段落？不对，原文的每个自然段落是怎样的？看原文的标点，其实是多个句子组成的段落，但用户说“每个自然段落用
包裹”，所以需要把原文的自然段落分开。等一下，原文的自然段落其实是： 原文的内容是： 第一部分：“Numbers computed from trained models, such as an example's difficulty or a model's benchmark lead, change with seed and prompt. We ask when such measurements can be corrected or compared. Noise shrinks correlations, and the textbook correction divides by the square root of two estimated reliabilities. On two CIFAR-100 difficulty scores, it returned correlations above one in 70 of 100 classes. We prove that its mean squared error is infinite even when the correlation and the reliabilities use separate seeds. Our rule corrects only when both reliabilities pass a significance test, then divides by their upper confidence bounds. Its output is always bounded, and its coverage guarantees assume Gaussian noise. It helps once reliability is well estimated, more than halving the error with 500 simulated units.” 这是一个自然段落。 第二部分：“Prompt settings can reverse rankings. On ARC, Qwen2-1.5B trails Llama-3.2-3B by 3.8 points with no examples in the prompt and leads by 16.6 with two. Across four benchmarks, averaging the two settings names one winner for 15 of 16 such reversals. We compare models within each setting, controlling family-wise error asymptotically in the number of items for a fixed prompt grid.” 这是第二个自然段落。 第三部分：“Aligning embedding coordinates across seeds raises held-out per-dimension reliability from 0.003 to 0.813.” 这是第三个自然段落。 现在，规则2说长文用
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划分小节，但原文没有小节标题，所以可能不需要，因为不能增删改内容。那直接按自然段落用
包裹，然后重要的句子用。 首先，第一个自然段落： 里面有重要的内容，比如“Numbers computed from trained models, such as an example's difficulty or a model's benchmark lead, change with seed and prompt. We ask when such measurements can be corrected or compared. Noise shrinks correlations, and the textbook correction divides by the square root of two estimated reliabilities. On two CIFAR-100 difficulty scores, it returned correlations above one in 70 of 100 classes. We prove that its mean squared error is infinite even when the correlation and the reliabilities use separate seeds. Our rule corrects only when both reliabilities pass a significance test, then divides by their upper confidence bounds. Its output is always bounded, and its coverage guarantees assume Gaussian noise. It helps once reliability is well estimated, more than halving the error with 500 simulated units.” 这里重要的句子比如“We ask when such measurements can be corrected or compared.”、“We prove that its mean squared error is infinite even when the correlation and the reliabilities use separate seeds.”、“Our rule corrects only when both reliabilities pass a significance test, then divides by their upper confidence bounds.”、“It helps once reliability is well estimated, more than halving the error with 500 simulated units.” 这些可以用。 然后第二个自然段落：“Prompt settings can reverse rankings. On ARC, Qwen2-1.5B trails Llama-3.2-3B by 3.8 points with no examples in the prompt and leads by 16.6 with two. Across four benchmarks, averaging the two settings names one winner for 15 of 16 such reversals. We compare models within each setting, controlling family-wise error asymptotically in the number of items for a fixed prompt grid.” 重要的句子比如“Prompt settings can reverse rankings.”、“Across four benchmarks, averaging the two settings names one winner for 15 of 16 such reversals.” 第三个自然段落：“Aligning embedding coordinates across seeds raises held-out per-dimension reliability from 0.003 to 0.813.” 可以用。 现在，开始排版： 首先第一个段落：
Numbers computed from trained models, such as an example's difficulty or a model's benchmark lead, change with seed and prompt. We ask when such measurements can be corrected or compared. Noise shrinks correlations, and the textbook correction divides by the square root of two estimated reliabilities. On two CIFAR-100 difficulty scores, it returned correlations above one in 70 of 100 classes. We prove that its mean squared error is infinite even when the correlation and the reliabilities use separate seeds. Our rule corrects only when both reliabilities pass a significance test, then divides by their upper confidence bounds. Its output is always bounded, and its coverage guarantees assume Gaussian noise. It helps once reliability is well estimated, more than halving the error with 500 simulated units.
第二个段落：Prompt settings can reverse rankings. On ARC, Qwen2-1.5B trails Llama-3.2-3B by 3.8 points with no examples in the prompt and leads by 16.6 with two. Across four benchmarks, averaging the two settings names one winner for 15 of 16 such reversals. We compare models within each setting, controlling family-wise error asymptotically in the number of items for a fixed prompt grid.
第三个段落：Aligning embedding coordinates across seeds raises held-out per-dimension reliability from 0.003 to 0.813.
检查是否符合规则： 1. 只输出HTML片段，没有Markdown围栏，没有解释。 2. 每个自然段落用包裹，长文有没有用