Global Evolutionary Steering: Refining Activation Steering Control via Cross-Layer Consistency
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917336162762752 |
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| author | Jiang, Xinyan Yu, Wenjing Wang, Di Hu, Lijie |
| author_facet | Jiang, Xinyan Yu, Wenjing Wang, Di Hu, Lijie |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_12298 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Global Evolutionary Steering: Refining Activation Steering Control via Cross-Layer Consistency Jiang, Xinyan Yu, Wenjing Wang, Di Hu, Lijie Machine Learning Artificial Intelligence 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. |
| title | Global Evolutionary Steering: Refining Activation Steering Control via Cross-Layer Consistency |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2603.12298 |