Mechanistic Interpretability

Mechanistic interpretability is the science of understanding what neural networks actually learn and how they make decisions. At GVN, we don't just use AI — we dissect it. Our interpretability research goes beyond surface-level explainability. We reverse-engineer the internal representations of neural networks to identify circuits, features, and failure modes that traditional testing misses. This work is critical for building AI systems that are safe, trustworthy, and aligned with human values. Our research covers: - Circuit-level analysis of transformer architectures - Feature visualization and attribution methods - Automated interpretability at scale - Safety-critical AI validation and red-teaming - Alignment research and value learning - Superposition and polysemanticity analysis