Global Evolutionary Steering: Refining Activation Steering Control via Cross-Layer Consistency

ArXi:2603.12298v1 Announce Type: cross Activation engineering enables precise control over Large Language Models (LLMs) without the computational cost of fine-tuning. However, existing methods deriving vectors from static activation differences are susceptible to high-dimensional noise and layer-wise semantic drift, often capturing spurious correlations rather than the target intent. To address this, we propose Global Evolutionary Refined Steering (GER-steer), a