AI interpretability · Jacobian Lens · alignment
Research explainers on J-Space and the internal workspaces where modern LLMs stage reportable reasoning.
How safety teams use interpretability to catch behaviors that tests miss — and make model behavior operational.
Read featured explainer →Deep dives on Anthropic’s interpretability work, internal workspaces in LLMs, and what J-Space reveals about model behavior.
Rosetta Neurons scaling paper review: shared neurons in 80M-30B parameter models grow as a sublinear power law (exponent 0.5-0.7), so their share falls.
The definition that answers the first part of the question is in the section “What about consciousness?”
GN on the arithmetic prompts adds Gaussian noise to the token embeddings of the third example’s operands, X 3 and Y 3.
That expression formalizes the prose definition. The PDF text used here does not display this equation.
How the Activation Controllability Benchmark paper works: a fixed copy task, a 0.85 similarity keep-rule, and residual-stream concept projections.
Gao et al.’s logit-diff ablation score measures how sparse an SAE latent’s effect on output logits is. It is not a rule for admitting circuit components.
J-Space tracks the sparse internal workspace where language models stage reportable reasoning. The Jacobian Lens makes that workspace measurable — so safety, alignment, and interpretability research can move from speculation to evidence.