包裹，原文看起来是一个长段落，但里面有不同的部分，比如背景、问题、方法的两个层面、耦合机制、实验结果，可能需要拆分吗？不对，原文是连续的，但用户说长文用
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划分小节，得看看有没有可以划分的地方。比如第一部分是背景和问题：“Learning with noisy labels has been extensively studied... necessitating reduced reliance on exact labels and instead leveraging ordinal structure among samples.” 这部分是问题陈述，可以作为一节？然后方法部分：“These requirements call for methods... addressing noise beyond the capacity of local residual refinement.” 这里提出GRAIN的两个层面，然后耦合机制：“The two levels are coupled via... cannot resolve.” 然后实验结果：“Experimental results show...” 首先，第一句背景：“Learning with noisy labels has been extensively studied for classification, yet its regression counterpart remains relatively underexplored.” 可以作为开头的段落。然后下一句讲回归和分类的不同，噪声的特点：“Unlike classification, regression involves continuous-valued targets where label noise exhibits heterogeneous magnitudes across instances, which poses conflicting requirements on the model: correcting small-to-moderate deviations requires sensitivity to label values for instance-level refinement, whereas large deviations caused by corrupted or outlying annotations render exact label values unreliable, necessitating reduced reliance on exact labels and instead leveraging ordinal structure among samples.” 这部分是问题的核心，可以用
划分吗？比如
问题背景与挑战
，然后下面的。然后“These requirements call for methods that capture both global structure and sample-specific deviations in a unified manner.” 这是过渡，然后提出GRAIN：“To this end, we propose GRAIN, a dual-granularity framework for robust noisy regression.” 然后分两个层面：
细粒度层面：残差学习机制
，然后At the fine-grained level... decouple label noise from predictive signals.
，然后粗粒度层面：分箱对比正则化
，At the coarse-grained level... local residual refinement.
，然后耦合机制：“The two levels are coupled via a histogram-based bridging mechanism that enables bidirectional information flow: coarse-grained structural guidance establishes a well-ordered feature space that anchors residual learning, while fine-grained residual refinement corrects instance-level deviations that structural regularization alone cannot resolve.” 这部分可以用双粒度耦合机制
，然后。最后实验结果：“Experimental results show that GRAIN consistently improves robustness and generalization under diverse noise conditions.” 用
。 需要确认是否保留所有信息，不能增删改，所以划分小节的时候只是用标题，不改变内容。比如：
问题背景与挑战
Learning with noisy labels has been extensively studied for classification, yet its regression counterpart remains relatively underexplored. Unlike classification, regression involves continuous-valued targets where label noise exhibits heterogeneous magnitudes across instances, which poses conflicting requirements on the model: correcting small-to-moderate deviations requires sensitivity to label values for instance-level refinement, whereas large deviations caused by corrupted or outlying annotations render exact label values unreliable, necessitating reduced reliance on exact labels and instead leveraging ordinal structure among samples.
These requirements call for methods that capture both global structure and sample-specific deviations in a unified manner.
GRAIN框架设计
细粒度层面：残差学习机制
To this end, we propose GRAIN, a dual-granularity framework for robust noisy regression. At the fine-grained level, we derive an explicit residual learning mechanism from a robust loss formulation, introducing instance-specific auxiliary variables to decouple label noise from predictive signals.
粗粒度层面：分箱对比正则化
At the coarse-grained level, we introduce a binning-based contrastive regularization that enforces an order-preserving feature structure aligned with label proximity, addressing noise beyond the capacity of local residual refinement.
双粒度耦合机制
The two levels are coupled via a histogram-based bridging mechanism that enables bidirectional information flow: coarse-grained structural guidance establishes a well-ordered feature space that anchors residual learning, while fine-grained residual refinement corrects instance-level deviations that structural regularization alone cannot resolve.
Experimental results show that GRAIN consistently improves robustness and generalization under diverse noise conditions.
等一下，“To this end, we propose GRAIN...” 这句应该放在框架的开头，所以GRAIN框架设计
下面的第一句就是这个，然后分两个层面。对，刚才的细粒度层面里面包含了提出GRAIN的句子，是对的。 再检查一遍：原文中的所有信息都保留了，没有增删改。划分的小节标题是否合适？比如“问题背景与挑战”覆盖了开头的背景和问题，“GRAIN框架设计”覆盖了方法和耦合机制，下面的h3是框架的两个层面和耦合。这样符合用户的要求：长文用/
划分小节。 然后每个自然段落用
包裹，比如第一部分的问题背景是一个
，然后过渡句“These requirements call for...”是一个
，然后框架部分的每个层面是一个
，耦合机制是一个
，最后实验结果是一个
。 有没有重要的句子需要用？比如“we propose GRAIN, a dual-granularity framework for robust noisy regression.” 这个是核心，可以用吗？用户说重要句子可用，所以可以加：
To this end, we propose GRAIN, a dual-granularity framework for robust noisy regression. At the fine-grained level...
。还有“Experimental results show that GRAIN consistently improves robustness and generalization under diverse noise conditions.” 这句是结果，也可以用吗？比如Experimental results show that GRAIN consistently improves robustness and generalization under diverse noise conditions.
。 这样修改后更符合用户的要求。再检查一遍：问题背景与挑战
Learning with noisy labels has been extensively studied for classification, yet its regression counterpart remains relatively underexplored. Unlike classification, regression involves continuous-valued targets where label noise exhibits heterogeneous magnitudes across instances, which poses conflicting requirements on the model: correcting small-to-moderate deviations requires sensitivity to label values for instance-level refinement, whereas large deviations caused by corrupted or outlying annotations render exact label values unreliable, necessitating reduced reliance on exact labels and instead leveraging ordinal structure among samples.
These requirements call for methods that capture both global structure and sample-specific deviations in a unified manner.
GRAIN框架设计
细粒度层面：残差学习机制
To this end, we propose GRAIN, a dual-granularity framework for robust noisy regression. At the fine-grained level, we derive an explicit residual learning mechanism from a robust loss formulation, introducing instance-specific auxiliary variables to decouple label noise from predictive signals.
粗粒度层面：分箱对比正则化
At the coarse-grained level, we introduce a binning-based contrastive regularization that enforces an order-preserving feature structure aligned with label proximity, addressing noise beyond the capacity of local residual refinement.
双粒度耦合机制
The two levels are coupled via a histogram-based bridging mechanism that enables bidirectional information flow: coarse-grained structural guidance establishes a well-ordered feature space that anchors residual learning, while fine-grained residual refinement corrects instance-level deviations that structural regularization alone cannot resolve.
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