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.
An extracted subgraph is not yet a causal circuit. Read it as causal only after the paper’s own intervention controls are fixed, and do not fill parameters…
These are three different early-token procedures, not three placements of one prefix. In Ding, Li, and Zhang, ETA ( arXiv:2410.06625v2 , ICLR 2025; version…
In arXiv:2602.13483v2, ACC++ forms one rank-1 candidate for each pair (upstream component, singular direction of the head’s QK matrix Ω), builds the…
In Huang et al., arXiv:2608.11295v1, the post- </think> probe token, the 64–160-example backdoor and decontamination corpus, and the top-10 layerwise…
GPT-4 writes natural-language explanations of 307,200 GPT-2 XL MLP neurons across 48 layers, then scores them by simulating activations on held-out text.
Read workspace talk as residual bottlenecks, not a psychological workspace. Each layer writes a d_model update; softmax does many-to-few selection.
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.