Mechanistic Interpretability: Beyond the Black Box

When we talk about AI safety and transparency, we often hear about "explainable AI." But there's a significant difference between generating post-hoc explanations for model outputs and actually understanding the internal mechanisms that produce those outputs. Mechanistic interpretability is the latter. It's the science of reverse-engineering neural networks at the circuit level — identifying the features, circuits, and computational patterns that models learn during training. At GVN, our interpretability research focuses on several key areas: **Circuit Analysis**: Identifying the specific neural circuits responsible for particular behaviors in transformer models. This helps us understand not just what a model does, but how and why. **Feature Visualization**: Making the internal representations of neural networks visible and interpretable. When we can see what features a model has learned, we can better predict where it will succeed and where it will fail. **Safety Validation**: Using interpretability techniques to validate that AI systems behave as intended, even in edge cases that traditional testing might miss. This work matters because the AI systems of tomorrow will make decisions that affect people's lives. Understanding those systems deeply — not just their inputs and outputs, but their internal reasoning — is essential for building AI that is genuinely trustworthy.