First systematic test shows activation steering robustness drops sharply (up to 64%) under adversarial input perturbations across multiple extraction methods, models, and personas.
C og S teer: Cognition-Inspired Selective Layer Intervention for Efficiently Steering Large Language Models
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TALAN inserts a trainable latent memory path that remixes sequence information into small orthogonal perturbations, delivering 1.41-1.85 point average gains over matched LoRA and DoRA on four Qwen backbones and STEM/code benchmarks while adding under 1% parameters.
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Adversarial Robustness of Activation Steering in Large Language Models
First systematic test shows activation steering robustness drops sharply (up to 64%) under adversarial input perturbations across multiple extraction methods, models, and personas.
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TALAN: Task-Aligned Latent Adaptation Networks for Targeted Post-Training of Large Language Models
TALAN inserts a trainable latent memory path that remixes sequence information into small orthogonal perturbations, delivering 1.41-1.85 point average gains over matched LoRA and DoRA on four Qwen backbones and STEM/code benchmarks while adding under 1% parameters.