When you talk to an AI model like Claude, you talk to it in words. Internally, Claude processes those words as long lists of numbers, before again producing words as its output. These numbers in the middle are called activations— and like neural activity in the human brain, they encode Claude’s thoughts. Also like neural activity, activations are difficult to understand. We can’t easily decode them to read Claude’s thoughts. Over the past few years, we’ve developed a range of tools (like sparse autoencoders and attribution graphs ) for better understanding activations. These tools have taught us a great deal, but they don’t speak for themselves—their outputs are still complex objects that trained researchers need to carefully interpret. Today, we’re introducing a method for understanding activations that does speak for itself—literally. Our method, Natural Language Autoencoders (NLAs), converts an activation into natural-language text we can read directly.…