Depth-Aware Activation Steering via Attention Residuals

Activation steering is a simple, training-free way to modulate LLM behavior, yet existing methods typically inject vectors at a single layer and lack mechanisms for depth-aware propagation or internal verification. We combine two recent ideas - Attention Residuals (AttnRes) providing learnable cross-layer pathways, and Jacobian-lens (J-space) concept readout - into a lightweight depth-aware steering pipeline.On two small open models (Qwen3-0.6B and Qwen3.5-0.8B), distributing a persona vector according to AttnRes-style depth weights yields stronger internal alignment (deeper-layer concept cosine and rank, p<0.0001) and stronger behavioral shifts than single-layer injection. The effect is sign-specific: reverse vectors invert all six metrics, random directions degrade alignment (p<0.0001), and a short LoRA fine-tune erases the imprint. Injection rewires attention strictly downstream of the injection point (upstream JS = 0.00000).Three practical findings follow. First, a layer-wise geometric decomposition of cosine readouts: at the injection layer 123% of the increase is arithmetic, while 5-11 layers downstream 100% is dynamical. Second, an intensity window: effects are monotone over 0.3-0.7, then generation collapses (617 to 306 to 25 chars). Third, metric discipline: keyword-rule metrics fail LLM-judge validation (kappa <= 0.09), and two rule-based conclusions were retracted. Training the depth weights on a steering objective (200 steps, frozen backbone) raises final alignment 10x on held-out prompts. Cross-persona vectors retain 96-99% of the effect: specificity holds against random, not semantic, directions.We contribute a practical, low-overhead depth-aware steering method with a careful evaluation protocol. Code and data are released.