[2603.01281] Spectral Attention Steering for Prompt Highlighting

Computer Science > Computation and Language arXiv:2603.01281 (cs) [Submitted on 1 Mar 2026] Title:Spectral Attention Steering for Prompt Highlighting Authors:Weixian Waylon Li, Yuchen Niu, Yongxin Yang, Keshuang Li, Tiejun Ma, Shay B. Cohen View a PDF of the paper titled Spectral Attention Steering for Prompt Highlighting, by Weixian Waylon Li and 5 other authors View PDF HTML (experimental) Abstract:Attention steering is an important technique for controlling model focus, enabling capabilities such as prompt highlighting, where the model prioritises user-specified text. However, existing attention steering methods require explicit storage of the full attention matrix, making them incompatible with memory-efficient implementations like FlashAttention. We introduce Spectral Editing Key Amplification (SEKA), a training-free steering method that tackles this by directly editing key embeddings before attention computation. SEKA uses spectral decomposition to steer key embeddings towards latent directions that amplify attention scores for certain tokens. We extend this to Adaptive SEKA (AdaSEKA), a query-adaptive variant that uses a training-free routing mechanism to dynamically combine multiple expert subspaces based on the prompt's semantic intent. Our experiments show both methods significantly outperform strong baselines on standard steering benchmarks while adding much lower latency and memory overhead, in compatibility with optimised attention. Comments: Subjects: Computa...