Title: SALVE: Sparse Autoencoder-Latent Vector Editing for Mechanistic Control of Neural Networks
Primary area: interpretability and explainable AI
Keywords: Mechanistic Interpretability, Model Editing, Feature Visualization, Representation Learning

Abstract: Deep neural networks achieve impressive performance but remain difficult to interpret and control. We present \textbf{SALVE} (Sparse Autoencoder-Latent Vector Editing), a unified "discover, validate, and control" framework that bridges mechanistic interpretability and model editing. Using an $\ell_1$-regularized autoencoder, we learn a sparse, model-native feature basis without supervision. We validate these features with Grad-FAM, a feature-level saliency mapping method that visually grounds latent features in input data. Leveraging the autoencoder's structure, we perform precise and permanent weight-space interventions, enabling continuous modulation of both class-defining and cross-class features. We further derive a critical suppression threshold, $\alpha_{\text{crit}}$, quantifying each class’s reliance on its dominant feature, supporting fine-grained robustness diagnostics. Our approach is validated on both convolutional (ResNet-18) and transformer-based (ViT-B/16) models, demonstrating consistent, interpretable control over their behavior. This work contributes a principled methodology for turning feature discovery into actionable model edits, advancing the development of transparent and controllable AI systems.

TL;DR: We introduce a framework that uses a sparse autoencoder for precise, permanent, and continuous weight editing in CNNs and ViTs, offering fine-grained control over model behavior