All explainers

Deep dives on Anthropic’s interpretability work, internal workspaces in LLMs, and what J-Space reveals about model behavior.

Which paper-stated controls must a reader fix before treating an extracted circuit as a causal subgraph rather than a correlation: for Vi-CD (arXiv:2604.14477v1), clean versus foreground-segmentation-and-inpaint corrupted pairs, a task metric distinct from the target-logit-difference pruning criterion, and same-size random or non-circuit edge ablations on supervised ViT-B and OpenCLIP ViT-B/32; and for WeightLens and CircuitLens (arXiv:2510.14936v2), single-token forward-pass validation of weight-outlier tokens plus attention-head and logit attributions before an explainer description — and which of checkpoint, layer, threshold, epsilon, and dataset stay explicit unknowns rather than results to assume?

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…

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A research lens on internal model cognition

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.