这篇论文发现了SAE解释模型时的两个大坑——特征分裂和吸收，然后用C^2R正则化给治好了，代码开源，直接上手试试。
稀疏自编码器（SAE）常用于解释大语言模型，但扩展到大规模字典时会出现特征分裂（将连贯概念拆分为非原子潜在变量）和特征吸收（在通用特征中创建任意例外）问题。这些问题源于不同样本间的潜在变量分配不一致。C^2R方法通过惩罚批量中方向相似潜在变量的共激活，显式鼓励每个语义特征由统一潜在变量表示。评估显示C^2R有效缓解分裂和吸收，同时保持重建保真度，代码已在GitHub开源。
C$^{2}$R: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders
Sparse Autoencoders (SAEs) are widely used to interpret large language models by decomposing activations into sparse, human-understandable features, but scaling to large dictionaries exposes fundamental challenges. Systematic studies reveal pervasive feature splitting that fragments coherent concepts into non-atomic latents and widespread feature absorption that creates arbitrary exceptions in general features, severely compromising latent reliability. These issues stem from inconsistent latent assignment across samples: without cross-sample constraints, per-sample optimization often allows a single underlying concept to be inconsistently distributed across multiple redundant or interfering latents. To address this, we introduce C$^2$R (\underline{\textbf{C}}ross-sample \underline{\textbf{C}}onsistency \underline{\textbf{R}}egularization). C$^2$R explicitly encourages that each semantic feature is consistently represented by a unified latent across the batch by penalizing the co-activation of directionally similar latents. Comprehensive evaluation demonstrates that C$^2$R effectively mitigates both splitting and absorption while, crucially, preserving reconstruction fidelity, providing a principled solution that enhances latent interpretability without degrading model performance. Source code is available at https://github.com/hr-jin/Cross-sample-Consistency-Regularization.