Anthropic researchers built a tool called the Jacobian lens to inspect parts of Claude's internal computation as the model reasons through prompts and tasks. The work gives the company one of its clearest looks yet at how concepts are represented inside a frontier model.
The technique matters because model behavior is still difficult to explain, even for developers who train and deploy the systems. Better interpretability tools could help teams investigate failures, safety issues, and unexpected capabilities before models are widely released.
The findings also show why AI safety work is moving beyond output testing. Understanding where and how a model forms intermediate ideas could become as important as measuring whether the final answer looks correct.