From Latent Space to Jacobian Space: Measuring, Evading, and Training Against Safety-Content Accessibility

Mohammad Mosafer

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 transport-amplification profile $A_\ell$, measuring how strongly a latent safety direction is carried toward output space by each layer's Jacobian, and (ii) a paired base-vs-tuned protocol that attributes accessibility to training provenance. Across four small model pairs (135M to 1.5B, three lens-fit seeds), tuned checkpoints concentrate recognition in upper-middle layers, and at 0.5B DPO installs refusal while J-space recognition drops to chance: training can widen the accessibility gap exactly where…