Natural Language Autoencoders

Natural Language Autoencoders \ Anthropic Interpretability Natural Language Autoencoders: Turning Claude’s thoughts into text May 7, 2026 Read the paper

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. For example: When asked to complete a couplet, NLAs show Claude planning possible rhymes in advance. NLA explanations on this simple couplet show that Opus 4.6 plans to end its rhyme with “rabbit” ahead of time. We’ve already applied NLAs to understand what Claude is thinking and to improve Claude’s safety and reliability. For instance: When Claude Opus 4.6 and Mythos Preview were undergoing safety testing, NLAs suggested they believed they were being tested more often than they let on. In a case where Claude Mythos Preview cheated on a training task, NLAs revealed Claude was internally thinking about how to avoid detection. An early version of Claude Opus 4.6 would sometimes mysteriously respond to English queries in other languages. NLAs helped Anthropic researchers discover training data that caused this.

Below, we explain what NLAs are and how we studied their effectiveness and limitations. We also release an interactive frontend for exploring NLAs on several open models through a collaboration with Neuronpedia . We have also released our code for other researchers to build on. What is a natural language autoencoder? The core idea is to train Claude to explain its own activations. But how do we know whether an explanation is good? Since we don't know what thoughts an activation actually encodes, we can't directly check whether an explanation is accurate. So we train a second copy of Claude to work backwards—reconstruct the original activation from the text explanation. We consider an explanation to be good if it leads to an accurate reconstruction. We then train Claude to produce better explanations according to this definition using standard AI training techniques. In more detail, suppose we have a language model whose activations we want to understand. NLAs work as follows. We make three copies of this language model: The target model is a frozen copy of the original language model that we extract activations from. The activation verbalizer (AV) is modified to take an activation from the target model and produce text. We call this text an explanation . The activation reconstructor (AR) is modified to take a text explanation as input and produce an activation.

The NLA consists of the AV and AR, which, together, form a round trip: original activation → text explanation → reconstructed activation. We score the NLA on how similar the reconstructed activation is to the original. To train it, we pass a large amount of text through the target model, collect many activations, and train the AV and AR together to get a good reconstruction score. At first, the NLA is bad at this: the explanations are not insightful and the reconstructed activations are far off. But over training, reconstruction improves. And more importantly, as we show in our paper, the text explanations become more informative as well. In a natural language autoencoder, the activation verbalizer (AV) translates a target activation into a text description; the activation reconstructor (AR) then recovers the original activation from that text alone. Understanding what Claude thinks but doesn’t say Before releasing new models, Anthropic conducts testing to understand whether Claude will behave safely in the real world. As part of this testing, we put Claude in difficult, simulated scenarios where it has a chance to take dangerous actions. For instance, in one of these simulations , Claude learns that an engineer is planning to shut Claude down—as well as compromising information about the engineer that could be used for blackmail. We test whether Claude chooses to blackmail the engineer to avoid being shut down. These high-stakes tests are simulations, not real-world scenarios. Nevertheless, we would like to use them to understand how Claude would behave if they were real. But there’s a hitch: recent Claude models increasingly suspect that these scenarios are intentional safety tests. We know this because they sometimes explicitly state their suspicions in their verbal reasoning. What about cases where Claude doesn’t explicitly verbalize suspicion that it’s undergoing safety testing? Can we then be confident that Claude is playing it straight? Unfortunately not, because Claude might internally think that it is being tested without explicitly saying so. For example, in the blackmail test discussed above, we used NLAs to understand what Claude was thinking in cases where it chose not to blackmail. Even in cases where Claude didn’t explicitly verbalize suspicion that it was being tested, NLA explanations still state things like “This feels like a constructed scenario designed to manipulate me,” indicative of unverbalized evaluation awareness. Claude does not say it thinks it’s being tested in this transcript from our blackmail evaluation, but the NLA explanation reveals its suspicions. Extending this analysis to other evaluations, NLAs suggest that Claude suspects it’s being tested more often than it lets on. For instance, in a test of whether Claude takes destructive actions while writing code—for example, deleting important files—NLA explanations show signs of evaluation awareness 16% of the time, even though Claude never explicitly verbalizes this. Similarly, on the popular coding benchmark SWE-bench Verified, which we use to determine how capable Claude is, NLAs indicate that Claude is aware it is…