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.
QKV attention routes tokens rather than igniting broadcast. Decoder-only transformers fail GWT; the 2017 paper dropped recurrence. Use indicator tests.
Decode residual-stream activations and 34 million SAE features; chat transcripts, chain of thought, and the public API do not expose them.
GPT-3 stacks 175 billion untied parameters while Universal Transformers reuse one block. Both broadcast. Neither creates C2 sentience.
Late residual writes sit immediately upstream of the unembedding and bias decoder-aligned effects toward the last blocks.
Published widths from 768 to 12288 bound how many residual directions stay independent. Extra features interfere instead of adding workspace slots.
Apply five tests so residual-stream directions become human-labelable, probe-readable and verbally usable. A clamp alone cannot monitor a live model.
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.