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arXiv · 2603.12298

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

Xinyan Jiang·Wenjing Yu·Di Wang·Lijie Hu
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Abstract

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 training-free framework that grounded in the geometric stability of the network's representation evolution. GER-steer exploits this global signal to rectify raw steering vectors, effectively decoupling robust semantic intent from orthogonal artifacts. Extensive evaluations confirm that GER-steer consistently outperforms baselines, delivering superior efficacy and generalization without layer-specific tuning, establishing a universal solution for reliable model alignment.

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Xinyan Jiang, Wenjing Yu, Di Wang, Lijie Hu. 2026-03-12. Global Evolutionary Steering: Refining Activation Steering Control via Cross-Layer Consistency. https://arxiv.org/abs/2603.12298

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