Safety Teams Decode AI Models Using Mechanistic Interpretability in 2026
Safety teams use mechanistic interpretability to reveal AI behaviors missed by tests, making safety operational in 2026.
Category AI Safety
Using J-space for monitoring misalignment, detecting off-rail behavior, sandbagging, jailbreaks, scalable oversight, implications for future safety research.
Jacobian Lens for AI Safety: Reading a Language Model’s Hidden Thoughts
In brief Mid-layer activations in language models are projected by the Jacobian lens through transport matrices derived from averaged input-output…
Jacobian Lens J-Lens Claude
On July 6, 2026, Anthropic published a paper that changed how we think about what language models know but never say aloud. The jacobian lens—named after the Jacobian matrix in calculus—gives researchers a tool to read the silent, internal neural…