想搞懂SAE特征到底抽没抽到抽象概念？这篇提出FNL指标，不用LLM打分就能测抽象度，还能帮你挑干预特征，实测提分4.6。
稀疏自编码器（SAE）常用于解释大语言模型行为，但区分表面词汇特征与真正高层特征一直困难。研究者提出特征非局域性（FNL），定义为SAE特征激活的逐位置影响熵，可在73%-84%的随机配对中正确区分上下文相关推理特征与词元驱动特征。在越狱缓解审计中，FNL发现多数有效特征实为低FNL的位置特征而非真正识别有害意图。在DeepSeek-R1-Distill-Llama-8B上，引导高FNL特征使MATH-500准确率提升4.6个百分点，优于低FNL特征，但效果因模型而异。
Measuring Semantic Abstractness of SAE Features via Nonlocality
Sparse autoencoders (SAEs) have helped uncover mechanistic explanations for LLM behaviours such as reasoning, jailbreaking etc., via understanding the corresponding task-relevant and causally effective features. To evaluate such mechanistic explanations, downstream studies must distinguish surface lexical features from genuinely high-level ones. However, neither an autointerp-based semantic description nor causal steering utility fully resolves the abstraction level of a feature. To this end, we introduce \emph{Feature Nonlocality} (FNL), defined as the entropy of the normalized per-position influence on an SAE feature's activation. We report that FNL correlates with existing LLM-based proxy metrics of feature semantic abstractness, and successfully distinguishes context-dependent reasoning features from token-driven ones, correctly assigning the higher FNL to the contextual feature in $73$--$84\%$ of randomly drawn pairs that consist of one contextual and one token-level feature. We demonstrate two downstream applications. We audit SAE-based features used for jailbreak mitigation and find surprisingly that most effective features are positional features with low FNL rather than genuinely recognizing harmful intents. We report that steering high-FNL features in DeepSeek-R1-Distill-Llama-8B improves MATH-500 accuracy by $4.6$ points over the unsteered model and outperforms steering low-FNL features, though the gains are model-specific. We conclude that FNL provides an LLM-independent, label-free, correlational witness of the abstraction level of an SAE feature, with applications in evaluating mechanistic explanations as well as selecting features for downstream interventions.