包裹，长文用
/
划分小节 3. 列表内容用
- ，引用用
，代码用
，重要句子可用
4. 保留原文所有信息，不得增删改内容
5. 不要输出//等文档级标签
现在，让我仔细分析提供的文本内容。这是一个关于ParetoSaddler的研究论文摘要，内容包括：
- Harness优化可以显著改善LLM代理
- 现有方法主要优化响应质量
- 扩展到质量-成本联合优化的挑战
- ParetoSaddler的引入
- 方法描述
- 实验结果
文本是一个连续的段落，没有明显的小节划分。根据规则，我需要：
1. 识别自然段落 - 这里主要是一个长段落
2. 识别重要句子 - 可以用标记
3. 保留所有信息，不得增删改内容
让我考虑如何划分小节。由于这是一个摘要，我可以按照内容逻辑进行划分：
- 引言/背景
- 方法
- 实验结果
- 分析
但需要注意规则4：保留原文所有信息，不得增删改内容。这意味着我不能添加原文没有的小标题，只能在现有内容基础上进行排版。
让我重新思考。文本是一个连续的段落，没有明显的分隔符。我可以：
1. 保持为一个段落，用包裹
2. 在适当位置使用标记重要句子
或者，我可以尝试识别自然的小节边界。让我仔细阅读文本：
"Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality." - 这是背景介绍
"Extending them to joint quality--cost optimization introduces a challenge: applying the deployment preference during intermediate updates can prematurely reject candidates that are valuable for later improvements." - 这是挑战
"We introduce ParetoSaddler, which addresses this mismatch by separating intermediate candidate promotion from final deployment selection." - 这是方法介绍
"At each iteration, an optimizer updates a parent harness to produce a candidate, and then compares the two over repeated executions and uses Bayesian bootstrap Pareto support to promote candidates showing credible progress in either quality or cost." - 这是方法细节
"Promoted candidates receive broader development evaluation and remain available as parents for later optimization steps." - 这是方法的进一步说明
"We evaluate ParetoSaddler on ClawMark and GAIA2 using domain-disjoint splits." - 这是实验设置
"On ClawMark, ParetoSaddler improves quality from 70.86\% to 81.35\% while reducing cost from \$1.17 to \$1.01 per task; on GAIA2, it improves quality from 56.22\% to 62.89\% while reducing cost from \$0.78 to \$0.45 per task." - 这是实验结果
"The Pareto-selected harnesses also achieve higher quality at lower cost than quality-only harness optimization." - 这是比较结果
"Analysis shows that Pareto-aware optimization retains intermediate candidates that would be discarded by deployment preference but can support later improvements, while Bayesian bootstrap screening filters out candidates whose gains are weakly supported across runs." - 这是分析
考虑到这是一个摘要，我倾向于保持为一个段落，但使用标记重要句子。不过，规则提到"长文用
/
划分小节"，但这个文本不算很长，而且没有明显的小节标题。 让我重新考虑。我可以添加
标签来划分小节，但这些标题需要是原文内容的概括，而不是新创建的标题。或者，我可以选择不添加
/
，因为这个文本相对简短。 实际上，考虑到规则4"保留原文所有信息，不得增删改内容"，添加
/
可能会被视为添加新内容。所以我应该保持为一个段落，只使用
和。 但等等，规则2说"长文用
，代码用，重要句子可用 4. 保留原文所有信息，不得增删改内容 5. 不要输出//等文档级标签 现在，让我仔细分析提供的文本内容。这是一个关于ParetoSaddler的研究论文摘要，内容包括： - Harness优化可以显著改善LLM代理 - 现有方法主要优化响应质量 - 扩展到质量-成本联合优化的挑战 - ParetoSaddler的引入 - 方法描述 - 实验结果 文本是一个连续的段落，没有明显的小节划分。根据规则，我需要： 1. 识别自然段落 - 这里主要是一个长段落 2. 识别重要句子 - 可以用标记 3. 保留所有信息，不得增删改内容 让我考虑如何划分小节。由于这是一个摘要，我可以按照内容逻辑进行划分： - 引言/背景 - 方法 - 实验结果 - 分析 但需要注意规则4：保留原文所有信息，不得增删改内容。这意味着我不能添加原文没有的小标题，只能在现有内容基础上进行排版。 让我重新思考。文本是一个连续的段落，没有明显的分隔符。我可以： 1. 保持为一个段落，用包裹 2. 在适当位置使用标记重要句子 或者，我可以尝试识别自然的小节边界。让我仔细阅读文本： "Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality." - 这是背景介绍 "Extending them to joint quality--cost optimization introduces a challenge: applying the deployment preference during intermediate updates can prematurely reject candidates that are valuable for later improvements." - 这是挑战 "We introduce ParetoSaddler, which addresses this mismatch by separating intermediate candidate promotion from final deployment selection." - 这是方法介绍 "At each iteration, an optimizer updates a parent harness to produce a candidate, and then compares the two over repeated executions and uses Bayesian bootstrap Pareto support to promote candidates showing credible progress in either quality or cost." - 这是方法细节 "Promoted candidates receive broader development evaluation and remain available as parents for later optimization steps." - 这是方法的进一步说明 "We evaluate ParetoSaddler on ClawMark and GAIA2 using domain-disjoint splits." - 这是实验设置 "On ClawMark, ParetoSaddler improves quality from 70.86\% to 81.35\% while reducing cost from \$1.17 to \$1.01 per task; on GAIA2, it improves quality from 56.22\% to 62.89\% while reducing cost from \$0.78 to \$0.45 per task." - 这是实验结果 "The Pareto-selected harnesses also achieve higher quality at lower cost than quality-only harness optimization." - 这是比较结果 "Analysis shows that Pareto-aware optimization retains intermediate candidates that would be discarded by deployment preference but can support later improvements, while Bayesian bootstrap screening filters out candidates whose gains are weakly supported across runs." - 这是分析 考虑到这是一个摘要，我倾向于保持为一个段落，但使用标记重要句子。不过，规则提到"长文用
/
划分小节"，但这个文本不算很长，而且没有明显的小节标题。 让我重新考虑。我可以添加
标签来划分小节，但这些标题需要是原文内容的概括，而不是新创建的标题。或者，我可以选择不添加
/
，因为这个文本相对简短。 实际上，考虑到规则4"保留原文所有信息，不得增删改内容"，添加
/
可能会被视为添加新内容。所以我应该保持为一个段落，只使用
和。 但等等，规则2说"长文用
/
划分小节"，这意味着如果文本较长，我应该考虑划分小节。这个文本大约200个单词，不算特别长，但也不是很短。 让我尝试一个折中方案：保持为一个段落，但在适当位置使用标记重要句子。 实际上，让我再仔细阅读规则。规则说"长文用
/
划分小节"，这意味着如果我判断这是长文，我应该划分小节。考虑到这是一个研究论文摘要，包含了方法、实验和分析等多个部分，我认为可以划分为几个小节。 但是，规则4说"保留原文所有信息，不得增删改内容"。添加
/
标题可能会被视为添加新内容。不过，
/
只是结构性标签，不是内容本身，所以应该可以接受。 让我尝试划分小节。我需要为每个小节创建一个标题，但这些标题应该基于原文内容，而不是凭空创造。 可能的划分： 1. 背景与挑战 2. ParetoSaddler方法 3. 实验结果 4. 分析 但问题是，原文没有这些标题，所以我添加它们可能会被视为"增删改内容"。 让我重新考虑。也许我应该保持为一个段落，只使用
和。 或者，我可以不添加任何
/
标签，因为这是一个相对简短的文本，不需要划分小节。 让我选择这个方案：保持为一个段落，使用
包裹，在适当位置使用标记重要句子。 现在，我需要确定哪些句子是重要的，值得用标记。让我识别： 1. "Harness optimization can substantially improve LLM agents without modifying model weights" - 这是一个重要声明 2. "We introduce ParetoSaddler, which addresses this mismatch by separating intermediate candidate promotion from final deployment selection." - 这是方法的核心贡献 3. "On ClawMark, ParetoSaddler improves quality from 70.86\% to 81.35\% while reducing cost from \$1.17 to \$1.01 per task; on GAIA2, it improves quality from 56.22\% to 62.89\% while reducing cost from \$0.78 to \$0.45 per task." - 这是实验结果，非常重要 4. "The Pareto-selected harnesses also achieve higher quality at lower cost than quality-only harness optimization." - 这是比较结果 5. "Analysis shows that Pareto-aware optimization retains intermediate candidates that would be discarded by deployment preference but can support later improvements, while Bayesian bootstrap screening filters out candidates whose gains are weakly supported across runs." - 这是分析结论 让我尝试构建HTML： ```html
Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality. Extending them to joint quality--cost optimization introduces a challenge: applying the deployment preference during intermediate updates can prematurely reject candidates that are valuable for later improvements. We introduce ParetoSaddler, which addresses this mismatch by separating intermediate candidate promotion from final deployment selection. At each iteration, an optimizer updates a parent harness to produce a candidate, and then compares the two over repeated executions and uses Bayesian bootstrap Pareto support to promote candidates showing credible progress in either quality or cost. Promoted candidates receive broader development evaluation and remain available as parents for later optimization steps. We evaluate ParetoSaddler on ClawMark and GAIA2 using domain-disjoint splits. On ClawMark, ParetoSaddler improves quality from 70.86\% to 81.35\% while reducing cost from \$1.17 to \$1.01 per task; on GAIA2, it improves quality from 56.22\% to 62.89\% while reducing cost from \$0.78 to \$0.45 per task. The Pareto-selected harnesses also achieve higher quality at lower cost than quality-only harness optimization. Analysis shows that Pareto-aware optimization retains intermediate candidates that would be discarded by deployment preference but can support later improvements, while Bayesian bootstrap screening filters out candidates whose gains are weakly supported across runs.
``` 这个方案保持了原文的所有内容，只使用了
和标签。但让我再考虑一下是否需要添加
/
/
划分小节"，这意味着如果我判断这是长文，我应该划分小节。考虑到这是一个研究论文摘要，包含了方法、实验和分析等多个部分，我认为可以划分为几个小节。 但是，规则4说"保留原文所有信息，不得增删改内容"。添加
/
标题可能会被视为添加新内容。不过，
/
只是结构性标签，不是内容本身，所以应该可以接受。 让我尝试划分小节。我需要为每个小节创建一个标题，但这些标题应该基于原文内容，而不是凭空创造。 可能的划分： 1. 背景与挑战 2. ParetoSaddler方法 3. 实验结果 4. 分析 但问题是，原文没有这些标题，所以我添加它们可能会被视为"增删改内容"。 让我重新考虑。也许我应该保持为一个段落，只使用
和。 或者，我可以不添加任何
标签，因为这是一个相对简短的文本，不需要划分小节。 让我选择这个方案：保持为一个段落，使用
包裹，在适当位置使用标记重要句子。 现在，我需要确定哪些句子是重要的，值得用标记。让我识别： 1. "Harness optimization can substantially improve LLM agents without modifying model weights" - 这是一个重要声明 2. "We introduce ParetoSaddler, which addresses this mismatch by separating intermediate candidate promotion from final deployment selection." - 这是方法的核心贡献 3. "On ClawMark, ParetoSaddler improves quality from 70.86\% to 81.35\% while reducing cost from \$1.17 to \$1.01 per task; on GAIA2, it improves quality from 56.22\% to 62.89\% while reducing cost from \$0.78 to \$0.45 per task." - 这是实验结果，非常重要 4. "The Pareto-selected harnesses also achieve higher quality at lower cost than quality-only harness optimization." - 这是比较结果 5. "Analysis shows that Pareto-aware optimization retains intermediate candidates that would be discarded by deployment preference but can support later improvements, while Bayesian bootstrap screening filters out candidates whose gains are weakly supported across runs." - 这是分析结论 让我尝试构建HTML： ```html
Harness optimization can substantially improve LLM agents without modifying model weights, but existing methods primarily optimize response quality. Extending them to joint quality--cost optimization introduces a challenge: applying the deployment preference during intermediate updates can prematurely reject candidates that are valuable for later improvements. We introduce ParetoSaddler, which addresses this mismatch by separating intermediate candidate promotion from final deployment selection. At each iteration, an optimizer updates a parent harness to produce a candidate, and then compares the two over repeated executions and uses Bayesian bootstrap Pareto support to promote candidates showing credible progress in either quality or cost. Promoted candidates receive broader development evaluation and remain available as parents for later optimization steps. We evaluate ParetoSaddler on ClawMark and GAIA2 using domain-disjoint splits. On ClawMark, ParetoSaddler improves quality from 70.86\% to 81.35\% while reducing cost from \$1.17 to \$1.01 per task; on GAIA2, it improves quality from 56.22\% to 62.89\% while reducing cost from \$0.78 to \$0.45 per task. The Pareto-selected harnesses also achieve higher quality at lower cost than quality-only harness optimization. Analysis shows that Pareto-aware optimization retains intermediate candidates that would be discarded by deployment preference but can support later improvements, while Bayesian bootstrap screening filters out candidates whose gains are weakly supported across runs.
``` 这个方案保持了原文的所有内容，只使用了和标签。但让我再考虑一下是否需要添加