As AI systems become more capable and have real-world impact on decisions in science, education, and healthcare, understanding how they work is essential. Interpretability refers to methods that help us understand why a model produced a given output. There are many ways we might achieve this.

For example, reasoning models are incentivized to explain their work on the way to a final answer. Chain of thought interpretability leverages these explanations to monitor the model’s behavior. This is immediately useful: current reasoning models’ chains of thought seem to be informative with respect to concerning behaviors like deception. However, fully relying on this property is a brittle strategy, and this may break down over time.

On the other hand, mechanistic interpretability, which is the focus of this work, seeks to completely reverse engineer a model’s computations. It has so far been less immediately useful, but in principle, could offer a more complete explanation of the model’s behavior. By seeking to explain model behavior at the most granular level, mechanistic interpretability can make fewer assumptions and give us more confidence. But the path from low-level details to explanations of complex behaviors is much longer and more difficult.

Interpretability supports several key goals, for example enabling better oversight and providing early warning signs of unsafe or strategically misaligned behavior. It also complements our other safety efforts, such as scalable oversight, adversarial training, and red-teaming.