The most popular way to interpret AI is missing the bigger picture.
Models think in curved shapes. But sparse autoencoders (SAEs) work with straight lines.
Can they still capture models’ curved neural geometry? Yes, but not how you might think! (1/7)
Neural networks might speak English, but they think in shapes.
Understanding their rich *neural geometry* is key to understanding how they work – and to debugging and controlling them with precision.
Starting today, we’re releasing a series of posts on this research agenda. 🧵