Activation steering has emerged as a cost-effective paradigm for modifying large language model (LLM) behaviors. Existing methods typically intervene at the block level, steering the bundled activations of selected attention heads, feedforward networks, or residual streams. However, we reveal that block-level activations are inherently heterogeneous, entangling beneficial, irrelevant, and harmful features, thereby rendering block-level steering coarse, inefficient, and intrusive. To investigate the root cause, we decompose block activations into fine-grained atomic unit (AU)-level activations, where each AU-level activation corresponds to a single dimension of the block activation, and each AU denotes a slice of the block weight matrix. Steering an AU-level activation is thus equivalent to steering its associated AU. Our theoretical and empirical analysis show that heterogeneity arises because different AUs or dimensions control distinct token distributions in LLM outputs. Hence, block-level steering inevitably moves helpful and harmful token directions together, which reduces efficiency. Restricting intervention to beneficial AUs yields more precise and effective steering. Building on this insight, we propose AUSteer, a simple and efficient method that operates at a finer granularity of the AU level. AUSteer first identifies discriminative AUs globally by computing activation momenta on contrastive samples. It then assigns adaptive steering strengths tailored to diverse inputs and selected AU activations. Comprehensive experiments on multiple LLMs and tasks show that AUSteer consistently surpasses advanced baselines while steering considerably fewer activations, demonstrating that steering less achieves more.

翻译：激活导向已成为一种经济高效地修改大型语言模型（LLM）行为的新范式。现有方法通常在模块层面进行干预，导向选定注意力头、前馈网络或残差流的捆绑式激活。然而，我们发现模块级激活本质上是异质的，混杂着有益、无关及有害的特征，从而导致模块级导向方法存在粗糙、低效和侵入性强的问题。为探究其根本原因，我们将模块激活分解为细粒度的原子单元（AU）级激活，其中每个AU级激活对应模块激活的单一维度，而每个AU表示模块权重矩阵的一个切片。因此，导向一个AU级激活等价于导向其关联的AU。我们的理论与实证分析表明，异质性产生的根源在于不同AU（即不同维度）控制着LLM输出中不同的词元分布。因此，模块级导向不可避免地会同时移动有益和有害的词元方向，从而降低效率。将干预限制在有益的AU上能实现更精准有效的导向。基于这一洞见，我们提出AUSteer——一种在AU粒度上操作的简洁高效方法。AUSteer首先通过计算对比样本上的激活动量来全局识别判别性AU，随后根据多样化输入和选定的AU激活分配合适的自适应导向强度。在多个LLM和任务上的综合实验表明，AUSteer在显著减少激活干预量的同时，持续优于现有先进基线方法，印证了“更少干预，更多成效”的理念。