Anthropic explains how language models reason

BLOT: The Anthropic team just gave us the clearest glimpse yet into how Claude organizes its thinking, and it starts with tracing latent directions in embedding space.

I’ve been watching Anthropic’s interpretability research evolve for a while, but this new post stands out. It’s worth reading. They’ve developed a method for tracing a language model’s internal thought process by identifying “latent directions” that map to specific concepts, like understanding whether Claude believes something is true or false. By nudging these directions, researchers can see how internal states influence downstream reasoning and even manipulate them to observe counterfactual behavior.

The big takeaway? Claude doesn’t just predict the next word. It builds complex internal representations that evolve over the course of a prompt. And by reverse-engineering these activations, Anthropic is getting closer to interpretable, steerable AI. This pushes us beyond vague attention heat maps into real causal understanding of model behavior.

This is early work, but the direction is clear. Interpretability is becoming tractable. If we can trace model beliefs, maybe we can align or debug them in real time too.

📎 Footnotes:

Anthropic, “Tracing Thoughts: How Language Models Reason,” https://www.anthropic.com/news/tracing-thoughts-language-model