Personalizing Large Language Models (LLMs) for long-term multi-turn interactions remains challenging, as user preferences and behavioral traits are progressively revealed and continuously evolving throughout conversations. Previous personalization methods have mostly relied on prompt-based or post-training (e.g., fine-tuning) paradigms; however, these approaches have been either prone to shallow alignment or have suffered from high computational costs. Inspired by the recent emergence of activation steering, we explore whether personalized alignment can be achieved through lightweight latent-space intervention without modifying backbone parameters. To achieve this goal, we propose PersonaSteer, a dynamic side-model architecture for multi-turn personalized alignment via learnable activation steering. Specifically, to enable fine-grained and evolving personalization, our design learns high-dimensional steering representations from unstructured interaction history and injects them into the backbone model’s residual stream for latent-space behavioral modulation. Furthermore, we introduce a steering-vector feedback loop to maintain persona consistency across multi-turn conversations. Extensive experiments demonstrate that PersonaSteer achieves best or top-tier alignment performance, averaging 3.18/5 on Qwen2.5-7B and 2.95/5 on Qwen2.5-3B. In addition, our framework approach maintains stability during evolving dialogues, with alignment performance consistently improving over time. PersonaSteer provides a lightweight solution for building truly dynamic and personalized LLM. The code is available in https://github.com/tsinghua-fib-lab/PersonaSteer.