From Latent Space to Jacobian Space: Measuring, Evading, and Training Against Safety-Content Accessibility
Output-only safety monitoring sees only the end of a model's computation, yet the model computes its answer before emitting it: what it is internally poised to say is safety-critical. Jacobian-space (J-space) readouts, linear maps from hidden states to the output vocabulary via the model's input-output Jacobian, have been proposed as a window on behaviorally accessible internal content, and a first safety protocol (JADR) showed that danger recognition is readable there. What remains unknown is how this accessible content relates to the latent-space safety geometry studied by representation engineering, and how safety training shapes it. We introduce two quantitative bridges: (i) a transpo...
| Authors | Mohammad Mosafer |
|---|---|
| Primary category | cs.AI |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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