When the Coffee Feature Activates on Coffins: An Analysis of Feature Extraction and Steering for Mechanistic Interpretability
Raphael Ronge, Markus Maier, Frederick Eberhardt
TL;DR
The paper critically evaluates Anthropic's mechanistic interpretability program by reproducing SAE-based feature extraction and steering on Llama 3.1, confirming basic capabilities while uncovering substantial fragility, context-sensitivity, and label-interpretation misalignments. It demonstrates that many features do not map cleanly to human concepts, that steering is highly layer- and coefficient-dependent, and that generalization across features and layers is limited. The work argues for shifting AI-safety focus from deciphering internal representations to ensuring reliable prediction and controlled output, highlighting unresolved challenges in using SAE-based features for safety-critical applications. Overall, while SAE-based MI can produce interpretable-looking demonstrations in specific cases, its robustness and universality remain insufficient for deployment in safety-critical settings.
Abstract
Recent work by Anthropic on Mechanistic interpretability claims to understand and control Large Language Models by extracting human-interpretable features from their neural activation patterns using sparse autoencoders (SAEs). If successful, this approach offers one of the most promising routes for human oversight in AI safety. We conduct an initial stress-test of these claims by replicating their main results with open-source SAEs for Llama 3.1. While we successfully reproduce basic feature extraction and steering capabilities, our investigation suggests that major caution is warranted regarding the generalizability of these claims. We find that feature steering exhibits substantial fragility, with sensitivity to layer selection, steering magnitude, and context. We observe non-standard activation behavior and demonstrate the difficulty to distinguish thematically similar features from one another. While SAE-based interpretability produces compelling demonstrations in selected cases, current methods often fall short of the systematic reliability required for safety-critical applications. This suggests a necessary shift in focus from prioritizing interpretability of internal representations toward reliable prediction and control of model output. Our work contributes to a more nuanced understanding of what mechanistic interpretability has achieved and highlights fundamental challenges for AI safety that remain unresolved.