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.
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.
Gao et al. (2024) cite the BERT interpretability illusion as a gloss: random activating examples can make an overly broad feature explanation look right.
In arXiv:2412.09565v2 (Bailey et al., 8 Feb 2025), an activation is obfuscated against a named latent-space monitor D, not obfuscated in the abstract.
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…
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.