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.
How Automated Red-Teaming Hides Misalignment The AI Alignment Forum reported, in a linkpost listing authors Oscar Balcells Obeso, Richard Qi, Monte MacDiarmid, and Evan Hubinger:
When features are sparse, superposition is the phenomenon of representing more features than there are dimensions.
A 2024 arXiv preprint frames neuron-to-neuron computation as transfer functions, clarifying what circuit analysis can claim about language models.
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.
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.