Title: Steering Prototypes with Prompt Tuning for Rehearsal-free Continual Learning. Abstract: Prototype, as a representation of class embeddings, has been explored to reduce memory footprint or avoid bias towards the latest task for continual learning. However, prototype-based methods still suffer from performance deterioration due to semantic drift and prototype interference. In this work, we propose a simple and novel framework for rehearsal-free continual learning. We show that task-specific prompt-tuning when coupled with a contrastive loss design can effectively address both issues and largely improves the potency of prototypes. The proposed framework excels at three challenging benchmarks, resulting in 3% to 6% absolute improvements over state-of-the-art methods without usage of a rehearsal buffer or a test-time oracle. Furthermore, the proposed framework largely bridges the performance gap between incremental learning and offline joint learning, demonstrating a promising design schema for continual learning.