SOURCE-LINKED INTELLIGENCE
Controllable Affective Generation via Latent Vector Steering
Large Language Models (LLMs) often produce emotionally flattened responses after alignment, limiting their effectiveness in affect-sensitive applications. In this paper, we propose EmoVec, a lightweight framework for controllable affective generation via latent vector steering. EmoVec extracts emotion-specific directions from paired neutral and emotion-conditioned responses using contrastive activation addition, and further refines them through task-specific debiasing and principal subspace removal. During inference, these vectors are injected into the final residual stream with static or scen
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-26T09:23:50.000Z
First collected: 2026-09-21T09:22:01.459Z. This is not the publication date.