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arXiv · 2603.10705

PRISM-$\Delta$: Differential Subspace Steering for Prompt Highlighting in Large Language Models

Yuyao Ge·Shenghua Liu·Yiwei Wang·Baolong Bi·Lingrui Mei·Jiayu Yao·Jiafeng Guo·Xueqi Cheng
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Abstract

Prompt highlighting steers a large language model to prioritize user-specified text spans during generation. A key challenge of existing Key-editing approaches is extracting steering directions that capture the difference between relevant and irrelevant contexts, rather than shared structural patterns common to both. We propose PRISM-$\Delta$ (Projection-based Relevance-Informed Steering Method), which decomposes the difference between positive and negative cross-covariance matrices to maximize discriminative energy while eliminating shared directions. Each attention head receives a continuous softplus importance weight, letting weak-but-useful heads contribute at reduced strength. The framework extends naturally to Value representations, capturing content-channel signal that Key-only methods leave unused. Across four benchmarks and five models, our methods match or exceed the best existing method on 19 of 20 configurations, with relative gains up to +13.0%, while halving the fluency cost of steering. Our methods also scale to long-context retrieval, outperforming the best existing method by up to +5.1% relative gain. PRISM-$\Delta$ is compatible with FlashAttention and adds negligible memory overhead.

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Yuyao Ge, Shenghua Liu, Yiwei Wang, Baolong Bi, Lingrui Mei, Jiayu Yao, Jiafeng Guo, Xueqi Cheng. 2026-03-11. PRISM-$\Delta$: Differential Subspace Steering for Prompt Highlighting in Large Language Models. https://arxiv.org/abs/2603.10705

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